diff --git a/include/rabitqlib/index/hnsw/hnsw.hpp b/include/rabitqlib/index/hnsw/hnsw.hpp
index 71b59e6..6e5b0af 100644
--- a/include/rabitqlib/index/hnsw/hnsw.hpp
+++ b/include/rabitqlib/index/hnsw/hnsw.hpp
@@ -1237,9 +1237,15 @@ inline void HierarchicalNSW::searchBaseLayerST_AdaptiveRerankOptDirect(
     float distk = 1e10;
 
     EstimateRecord start_estimate_record;
-    get_full_est_direct<Kernel>(
-        q_to_centroids, query_wrapper, ep_id, start_estimate_record
-    );
+    if (ex_bits_ > 0) {
+        get_full_est_direct<Kernel>(
+            q_to_centroids, query_wrapper, ep_id, start_estimate_record
+        );
+    } else {
+        get_bin_est_direct<Kernel>(
+            q_to_centroids, query_wrapper, ep_id, start_estimate_record
+        );
+    }
     float est_dist = start_estimate_record.est_dist;
     float low_dist = start_estimate_record.low_dist;
 
diff --git a/tests/unit/rabitqlib/index/hnsw_test.cpp b/tests/unit/rabitqlib/index/hnsw_test.cpp
new file mode 100644
index 0000000..f512624
--- /dev/null
+++ b/tests/unit/rabitqlib/index/hnsw_test.cpp
@@ -0,0 +1,44 @@
+#include "rabitqlib/index/hnsw/hnsw.hpp"
+
+#include <gtest/gtest.h>
+
+#include <cmath>
+#include <vector>
+
+namespace rabitqlib::hnsw {
+namespace {
+
+TEST(HnswOneBitSearchTest, UsesBinaryEstimateForEntryPoint) {
+    constexpr size_t dim = 64;
+    constexpr size_t count = 8;
+    std::vector<float> data(count * dim);
+    std::vector<float> centroid(dim);
+    std::vector<float> query(dim);
+
+    for (size_t i = 0; i < dim; ++i) {
+        centroid[i] = static_cast<float>(static_cast<int>(i % 13) - 6) / 5.0F;
+        query[i] = centroid[i];
+    }
+    for (size_t point = 0; point < count; ++point) {
+        for (size_t i = 0; i < dim; ++i) {
+            data[(point * dim) + i] =
+                centroid[i] + static_cast<float>((point + 1) * ((i % 11) + 1));
+        }
+    }
+
+    std::vector<PID> cluster_ids(count, 0);
+    HierarchicalNSW index(count, dim, 1, 2, 10, 100, METRIC_L2);
+    index.construct(1, centroid.data(), count, data.data(), cluster_ids.data(), 1, false);
+
+    const auto results = index.search(query.data(), 1, 1, 10, 1);
+
+    ASSERT_EQ(results.size(), 1U);
+    ASSERT_EQ(results[0].size(), 1U);
+    ASSERT_EQ(results[0][0].second, 0U);
+    EXPECT_TRUE(std::isfinite(results[0][0].first));
+    const float exact_distance = euclidean_sqr(query.data(), data.data(), dim);
+    EXPECT_NEAR(results[0][0].first, exact_distance, exact_distance * 0.1F);
+}
+
+}  // namespace
+}  // namespace rabitqlib::hnsw