diff --git a/faiss/utils/distances.cpp b/faiss/utils/distances.cpp
index e86cb2df3..60fc29d0c 100644
--- a/faiss/utils/distances.cpp
+++ b/faiss/utils/distances.cpp
@@ -563,7 +563,11 @@ struct Run_search_inner_product {
            size_t d,
            size_t nx,
            size_t ny) {
-        if (res.sel ||
+        // ny == 0 goes to the sequential path: it guards only on nx, so its
+        // per-query begin()/end() still runs and each handler writes its own
+        // neutral distance and -1 label. The BLAS path instead returns early
+        // on ny == 0, before the handler is initialized.
+        if (res.sel || ny == 0 ||
             nx * d < static_cast<size_t>(distance_compute_blas_threshold)) {
             exhaustive_inner_product_seq(x, y, d, nx, ny, res);
         } else {
@@ -582,7 +586,8 @@ struct Run_search_L2sqr {
            size_t nx,
            size_t ny,
            const float* y_norm2) {
-        if (res.sel ||
+        // See the note on ny == 0 in Run_search_inner_product.
+        if (res.sel || ny == 0 ||
             nx * d < static_cast<size_t>(distance_compute_blas_threshold)) {
             exhaustive_L2sqr_seq(x, y, d, nx, ny, res);
         } else {
diff --git a/tests/test_index.py b/tests/test_index.py
index d4105d6f1..52de4f73a 100644
--- a/tests/test_index.py
+++ b/tests/test_index.py
@@ -137,6 +137,32 @@ class TestIndexFlat(unittest.TestCase):
                     lims, np.zeros(1, dtype=np.int64)
                 )
 
+    def test_empty_index_with_blas(self):
+        # The threshold is what makes this a regression test: it puts the
+        # search in a configuration that would otherwise select the BLAS path,
+        # which returns early on an empty database without ever initializing
+        # the result handler. k covers the Top1, heap, and reservoir handlers.
+        saved_threshold = faiss.cvar.distance_compute_blas_threshold
+        faiss.cvar.distance_compute_blas_threshold = 1
+        try:
+            xq = np.arange(5, dtype="float32").reshape(-1, 1)
+            for metric_type in (faiss.METRIC_L2, faiss.METRIC_INNER_PRODUCT):
+                expected_distance = (
+                    np.finfo("float32").max
+                    if metric_type == faiss.METRIC_L2
+                    else np.finfo("float32").min
+                )
+                index = faiss.IndexFlat(1, metric_type)
+                for k in (1, 10, 150):
+                    with self.subTest(metric_type=metric_type, k=k):
+                        D = np.full((len(xq), k), 42, dtype="float32")
+                        I = np.full((len(xq), k), 99, dtype="int64")
+                        index.search(xq, k, D=D, I=I)
+                        np.testing.assert_array_equal(D, expected_distance)
+                        np.testing.assert_array_equal(I, -1)
+        finally:
+            faiss.cvar.distance_compute_blas_threshold = saved_threshold
+
 
 @for_all_simd_levels
 class TestDbParallelSearch(unittest.TestCase):