diff --git a/dev/specs/searchByEmbedding.spec.ts b/dev/specs/searchByEmbedding.spec.ts
new file mode 100644
index 0000000..a3f38ee
--- /dev/null
+++ b/dev/specs/searchByEmbedding.spec.ts
@@ -0,0 +1,276 @@
+import type { Payload } from 'payload'
+
+import { postgresAdapter } from '@payloadcms/db-postgres'
+import { type SerializedEditorState } from '@payloadcms/richtext-lexical/lexical'
+import { chunkRichText, chunkText } from 'helpers/chunkers.js'
+import { makeDummyEmbedDocs, makeDummyEmbedQuery, testEmbeddingVersion } from 'helpers/embed.js'
+import { createMockAdapter } from 'helpers/mockAdapter.js'
+import { getPayload } from 'payload'
+import payloadcmsVectorize, {
+  DbAdapter,
+  getVectorizedPayload,
+  type VectorizedPayload,
+} from 'payloadcms-vectorize'
+import { afterAll, beforeAll, describe, expect, test } from 'vitest'
+import { buildDummyConfig, DIMS, getInitialMarkdownContent } from './constants.js'
+import {
+  expectResultsOrderedByScore,
+  expectResultsRespectWhere,
+  expectValidVectorSearchResults,
+} from './helpers/vectorSearchExpectations.js'
+import {
+  BULK_QUEUE_NAMES,
+  createTestDb,
+  destroyPayload,
+  waitForVectorizationJobs,
+} from './utils.js'
+
+const embedFn = makeDummyEmbedQuery(DIMS)
+
+describe('searchByEmbedding method tests', () => {
+  let payload: Payload
+  let vectorizedPayload: VectorizedPayload
+  let adapter: DbAdapter
+  let markdownContent: SerializedEditorState
+  const titleAndQuery = 'My query is a title for searchByEmbedding'
+  const dbName = 'search_by_embedding_test'
+
+  beforeAll(async () => {
+    await createTestDb({ dbName })
+    adapter = createMockAdapter()
+
+    const config = await buildDummyConfig({
+      jobs: {
+        tasks: [],
+        autoRun: [
+          {
+            cron: '*/5 * * * * *', // Run every 5 seconds
+            limit: 10,
+          },
+        ],
+      },
+      collections: [
+        {
+          slug: 'posts',
+          fields: [
+            { name: 'title', type: 'text' },
+            { name: 'content', type: 'richText' },
+          ],
+        },
+      ],
+      db: postgresAdapter({
+        pool: {
+          connectionString: `postgresql://postgres:password@localhost:5433/${dbName}`,
+        },
+      }),
+      plugins: [
+        payloadcmsVectorize({
+          dbAdapter: adapter,
+          knowledgePools: {
+            default: {
+              collections: {
+                posts: {
+                  toKnowledgePool: async (doc, payload) => {
+                    const chunks: Array<{ chunk: string }> = []
+                    // Process title
+                    if (doc.title) {
+                      const titleChunks = chunkText(doc.title)
+                      chunks.push(...titleChunks.map((chunk) => ({ chunk })))
+                    }
+                    // Process content
+                    if (doc.content) {
+                      const contentChunks = await chunkRichText(doc.content, payload.config)
+                      chunks.push(...contentChunks.map((chunk) => ({ chunk })))
+                    }
+                    return chunks
+                  },
+                },
+              },
+              embeddingConfig: {
+                version: testEmbeddingVersion,
+                queryFn: makeDummyEmbedQuery(DIMS),
+                realTimeIngestionFn: makeDummyEmbedDocs(DIMS),
+              },
+            },
+          },
+          bulkQueueNames: BULK_QUEUE_NAMES,
+        }),
+      ],
+    })
+
+    payload = await getPayload({
+      config,
+      key: `search-by-embedding-test-${Date.now()}`,
+      cron: true,
+    })
+
+    const vp = getVectorizedPayload(payload)
+    if (!vp) {
+      throw new Error('Failed to get vectorized payload')
+    }
+    vectorizedPayload = vp
+
+    markdownContent = await getInitialMarkdownContent(config)
+  })
+
+  afterAll(async () => {
+    await destroyPayload(payload)
+  })
+
+  test('searchByEmbedding with embedding vector returns valid results', async () => {
+    // Create a post
+    const post = await payload.create({
+      collection: 'posts',
+      data: {
+        title: titleAndQuery,
+        content: markdownContent as unknown as any,
+      },
+    })
+
+    // Wait for vectorization jobs to complete
+    await waitForVectorizationJobs(payload)
+
+    // Get the embedding for our query
+    const queryEmbedding = await embedFn(titleAndQuery)
+    const embeddingArray = Array.isArray(queryEmbedding)
+      ? queryEmbedding
+      : Array.from(queryEmbedding)
+
+    // Search using the embedding directly
+    const results = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+    })
+
+    expectValidVectorSearchResults(results, {
+      checkShape: true,
+      expectedTitle: {
+        title: titleAndQuery,
+        postId: String(post.id),
+        embeddingVersion: testEmbeddingVersion,
+      },
+    })
+  })
+
+  test('searchByEmbedding results are ordered by score (highest first)', async () => {
+    // Get the embedding for our query
+    const queryEmbedding = await embedFn(titleAndQuery)
+    const embeddingArray = Array.isArray(queryEmbedding)
+      ? queryEmbedding
+      : Array.from(queryEmbedding)
+
+    const results = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+    })
+
+    expectResultsOrderedByScore(results)
+  })
+
+  test('searchByEmbedding respects limit parameter', async () => {
+    // Get the embedding for our query
+    const queryEmbedding = await embedFn(titleAndQuery)
+    const embeddingArray = Array.isArray(queryEmbedding)
+      ? queryEmbedding
+      : Array.from(queryEmbedding)
+
+    const limit = 2
+    const results = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+      limit,
+    })
+
+    expect(results.length).toBeLessThanOrEqual(limit)
+  })
+
+  test('searchByEmbedding respects where clause', async () => {
+    const sharedText = 'Shared searchable content for embedding search'
+
+    // Create two posts with same text
+    const post1 = await payload.create({
+      collection: 'posts',
+      data: {
+        title: sharedText,
+        content: null,
+      },
+    })
+
+    const post2 = await payload.create({
+      collection: 'posts',
+      data: {
+        title: sharedText,
+        content: null,
+      },
+    })
+
+    // Wait for vectorization jobs to complete
+    await waitForVectorizationJobs(payload)
+
+    // Get the embedding for our query
+    const queryEmbedding = await embedFn(sharedText)
+    const embeddingArray = Array.isArray(queryEmbedding)
+      ? queryEmbedding
+      : Array.from(queryEmbedding)
+
+    // Search without WHERE - should return both
+    const resultsAll = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+    })
+
+    expect(resultsAll.length).toBeGreaterThanOrEqual(2)
+
+    // Search with WHERE clause filtering by docId - should return only one
+    const resultsFiltered = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+      where: {
+        docId: { equals: String(post1.id) },
+      },
+    })
+
+    expectResultsRespectWhere(resultsFiltered, (r) => r.docId === String(post1.id))
+  })
+
+  test('searchByEmbedding with same embedding as search returns similar results', async () => {
+    const testQuery = 'test query for comparison'
+
+    // Create a post to search for
+    const post = await payload.create({
+      collection: 'posts',
+      data: {
+        title: testQuery,
+        content: null,
+      },
+    })
+
+    // Wait for vectorization jobs to complete
+    await waitForVectorizationJobs(payload)
+
+    // Get results using regular search
+    const searchResults = await vectorizedPayload.search({
+      knowledgePool: 'default',
+      query: testQuery,
+    })
+
+    // Get the embedding and use searchByEmbedding
+    const queryEmbedding = await embedFn(testQuery)
+    const embeddingArray = Array.isArray(queryEmbedding)
+      ? queryEmbedding
+      : Array.from(queryEmbedding)
+
+    const embeddingSearchResults = await vectorizedPayload.searchByEmbedding({
+      knowledgePool: 'default',
+      embedding: embeddingArray,
+    })
+
+    // Both should return results (not necessarily identical due to possible reranking in search)
+    expect(searchResults.length).toBeGreaterThan(0)
+    expect(embeddingSearchResults.length).toBeGreaterThan(0)
+
+    // The top result should be the same document chunk for both approaches
+    // since we're using the same embedding
+    expect(embeddingSearchResults[0].docId).toBe(searchResults[0].docId)
+  })
+})
diff --git a/src/endpoints/vectorSearch.ts b/src/endpoints/vectorSearch.ts
index 9a0b14b..fd1914f 100644
--- a/src/endpoints/vectorSearch.ts
+++ b/src/endpoints/vectorSearch.ts
@@ -44,6 +44,18 @@ export const createVectorSearchHandlers = (
     const reranked = await rerank.callback(query, candidates)
     return reranked.slice(0, effectiveLimit)
   }
+
+  const searchByEmbedding = async (
+    payload: BasePayload,
+    embedding: number[],
+    knowledgePool: KnowledgePoolName,
+    limit?: number,
+    where?: Where,
+  ) => {
+    // searchByEmbedding does not support reranking because rerankers need text, not vectors
+    return adapter.search(payload, embedding, knowledgePool, limit, where)
+  }
+
   const requestHandler: PayloadHandler = async (req) => {
     if (!req || !req.json) {
       return Response.json({ error: 'Request is required' }, { status: 400 })
@@ -77,5 +89,5 @@ export const createVectorSearchHandlers = (
       return Response.json({ error: `Internal server error: ${error}` }, { status: 500 })
     }
   }
-  return { vectorSearch, requestHandler }
+  return { vectorSearch, searchByEmbedding, requestHandler }
 }
diff --git a/src/index.ts b/src/index.ts
index 7621b9c..f716c0d 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -8,6 +8,7 @@ import type {
   KnowledgePoolDynamicConfig,
   VectorizedPayload,
   VectorSearchQuery,
+  VectorSearchEmbeddingQuery,
   BulkEmbedResult,
   RetryFailedBatchResult,
   DbAdapter,
@@ -76,6 +77,7 @@ export type {
 
   // For adapters
   VectorSearchResult,
+  VectorSearchEmbeddingQuery,
   EmbeddingRecord,
 } from './types.js'
 
@@ -357,6 +359,14 @@ export default (pluginOptions: PayloadcmsVectorizeConfig) =>
             params.limit,
             params.where,
           ),
+        searchByEmbedding: (params: VectorSearchEmbeddingQuery) =>
+          vectorSearchHandlers.searchByEmbedding(
+            payload,
+            params.embedding,
+            params.knowledgePool,
+            params.limit,
+            params.where,
+          ),
         findByIds: (params: {
           knowledgePool: KnowledgePoolName
           ids: string[]
diff --git a/src/types.ts b/src/types.ts
index 2fb10b1..e414e9b 100644
--- a/src/types.ts
+++ b/src/types.ts
@@ -57,6 +57,7 @@ export type VectorizedPayload = {
   _isBulkEmbedEnabled: (knowledgePool: KnowledgePoolName) => boolean
   getDbAdapterCustom: () => Record<string, any> | undefined
   search: (params: VectorSearchQuery) => Promise<Array<VectorSearchResult>>
+  searchByEmbedding: (params: VectorSearchEmbeddingQuery) => Promise<Array<VectorSearchResult>>
   findByIds: (params: {
     knowledgePool: KnowledgePoolName
     ids: string[]
@@ -349,6 +350,17 @@ export interface VectorSearchQuery {
   limit?: number
 }
 
+export interface VectorSearchEmbeddingQuery {
+  /** The knowledge pool to search in */
+  knowledgePool: KnowledgePoolName
+  /** The embedding vector to search with */
+  embedding: number[]
+  /** Optional Payload where clause to filter results. Can rely on embeddings collection fields or extension fields. */
+  where?: Where
+  /** Optional limit for number of results (default: 10) */
+  limit?: number
+}
+
 // ==========================================
 // Document types for internal collections
 // ==========================================