diff --git a/collection.go b/collection.go
index 68be66f..2a8a5a0 100644
--- a/collection.go
+++ b/collection.go
@@ -327,7 +327,8 @@ type Result struct {
 //
 //   - queryText: The text to search for. Its embedding will be created using the
 //     collection's embedding function.
-//   - nResults: The number of results to return. Must be > 0.
+//   - nResults: The maximum number of results to return. Must be > 0.
+//     There can be fewer results if a filter is applied.
 //   - where: Conditional filtering on metadata. Optional.
 //   - whereDocument: Conditional filtering on documents. Optional.
 func (c *Collection) Query(ctx context.Context, queryText string, nResults int, where, whereDocument map[string]string) ([]Result, error) {
@@ -348,7 +349,8 @@ func (c *Collection) Query(ctx context.Context, queryText string, nResults int,
 //   - queryEmbedding: The embedding of the query to search for. It must be created
 //     with the same embedding model as the document embeddings in the collection.
 //     The embedding will be normalized if it's not the case yet.
-//   - nResults: The number of results to return. Must be > 0.
+//   - nResults: The maximum number of results to return. Must be > 0.
+//     There can be fewer results if a filter is applied.
 //   - where: Conditional filtering on metadata. Optional.
 //   - whereDocument: Conditional filtering on documents. Optional.
 func (c *Collection) QueryEmbedding(ctx context.Context, queryEmbedding []float32, nResults int, where, whereDocument map[string]string) ([]Result, error) {
@@ -389,14 +391,26 @@ func (c *Collection) QueryEmbedding(ctx context.Context, queryEmbedding []float3
 		queryEmbedding = normalizeVector(queryEmbedding)
 	}
 
+	// If the filtering already reduced the number of documents to fewer than nResults,
+	// we only need to find the most similar docs among the filtered ones.
+	resLen := nResults
+	if len(filteredDocs) < nResults {
+		resLen = len(filteredDocs)
+	}
+
 	// For the remaining documents, get the most similar docs.
-	nMaxDocs, err := getMostSimilarDocs(ctx, queryEmbedding, filteredDocs, nResults)
+	nMaxDocs, err := getMostSimilarDocs(ctx, queryEmbedding, filteredDocs, resLen)
 	if err != nil {
 		return nil, fmt.Errorf("couldn't get most similar docs: %w", err)
 	}
 
-	res := make([]Result, 0, nResults)
-	for i := 0; i < nResults; i++ {
+	// As long as we don't filter by threshold, resLen should match len(nMaxDocs).
+	if resLen != len(nMaxDocs) {
+		return nil, fmt.Errorf("internal error: expected %d results, got %d", resLen, len(nMaxDocs))
+	}
+
+	res := make([]Result, 0, resLen)
+	for i := 0; i < resLen; i++ {
 		res = append(res, Result{
 			ID:         nMaxDocs[i].docID,
 			Metadata:   c.documents[nMaxDocs[i].docID].Metadata,
@@ -406,7 +420,6 @@ func (c *Collection) QueryEmbedding(ctx context.Context, queryEmbedding []float3
 		})
 	}
 
-	// Return the top nResults
 	return res, nil
 }