diff --git a/src/basic_memory/api/v2/routers/search_router.py b/src/basic_memory/api/v2/routers/search_router.py
index eefc82da..be80d4b4 100644
--- a/src/basic_memory/api/v2/routers/search_router.py
+++ b/src/basic_memory/api/v2/routers/search_router.py
@@ -14,7 +14,7 @@ from basic_memory.repository.semantic_errors import (
     SemanticDependenciesMissingError,
     SemanticSearchDisabledError,
 )
-from basic_memory.schemas.search import SearchQuery, SearchResponse
+from basic_memory.schemas.search import SearchQuery, SearchResponse, SearchRetrievalMode
 from basic_memory.deps import (
     SearchServiceV2ExternalDep,
     EntityServiceV2ExternalDep,
@@ -67,6 +67,7 @@ async def search(
         has_filters=bool(query.note_types or query.entity_types or query.metadata_filters),
     ):
         offset = (page - 1) * page_size
+        exact_count_available = query.retrieval_mode == SearchRetrievalMode.FTS
         try:
             with logfire.span(
                 "api.search.search.execute_query",
@@ -76,10 +77,14 @@ async def search(
                 page=page,
                 page_size=page_size,
             ):
-                results, total = await asyncio.gather(
-                    search_service.search(query, limit=page_size, offset=offset),
-                    search_service.count(query),
-                )
+                if exact_count_available:
+                    results, total = await asyncio.gather(
+                        search_service.search(query, limit=page_size, offset=offset),
+                        search_service.count(query),
+                    )
+                else:
+                    results = await search_service.search(query, limit=page_size + 1, offset=offset)
+                    total = 0
         except SemanticSearchDisabledError as exc:
             raise HTTPException(status_code=400, detail=str(exc)) from exc
         except SemanticDependenciesMissingError as exc:
@@ -94,7 +99,15 @@ async def search(
             phase="paginate_results",
             result_count=len(results),
         ):
-            has_more = offset + len(results) < total
+            if exact_count_available:
+                has_more = offset + len(results) < total
+            else:
+                # Trigger: semantic modes would need another vector/hybrid retrieval to count.
+                # Why: search requests should not pay for a second semantic pass.
+                # Outcome: preserve probe pagination for semantic search and leave total at 0.
+                has_more = len(results) > page_size
+                if has_more:
+                    results = results[:page_size]
 
         with logfire.span(
             "api.search.search.hydrate_results",
diff --git a/src/basic_memory/repository/search_repository_base.py b/src/basic_memory/repository/search_repository_base.py
index 6a7a0590..c5367295 100644
--- a/src/basic_memory/repository/search_repository_base.py
+++ b/src/basic_memory/repository/search_repository_base.py
@@ -260,25 +260,10 @@ class SearchRepositoryBase(ABC):
         retrieval_mode: SearchRetrievalMode = SearchRetrievalMode.FTS,
         min_similarity: Optional[float] = None,
     ) -> int:
-        """Count results for retrieval modes that cannot use a backend COUNT query."""
-        # Trigger: vector and hybrid modes rank after embedding lookup, filtering, and fusion.
-        # Why: that scoring pipeline is not expressible as a portable database COUNT query.
-        # Outcome: fetch the bounded candidate set and count the final in-memory results.
-        results = await self.search(
-            search_text=search_text,
-            permalink=permalink,
-            permalink_match=permalink_match,
-            title=title,
-            note_types=note_types,
-            after_date=after_date,
-            search_item_types=search_item_types,
-            metadata_filters=metadata_filters,
-            retrieval_mode=retrieval_mode,
-            min_similarity=min_similarity,
-            limit=VECTOR_FILTER_SCAN_LIMIT,
-            offset=0,
-        )
-        return len(results)
+        """Count results when a backend-specific COUNT query is available."""
+        if retrieval_mode != SearchRetrievalMode.FTS:
+            raise ValueError("Exact counts are only supported for full-text search retrieval.")
+        raise NotImplementedError("Backend search repositories must implement full-text counts.")
 
     # ------------------------------------------------------------------
     # Abstract methods — semantic search (backend-specific DB operations)
diff --git a/tests/api/v2/test_search_router.py b/tests/api/v2/test_search_router.py
index 58cc091a..a7f93ce2 100644
--- a/tests/api/v2/test_search_router.py
+++ b/tests/api/v2/test_search_router.py
@@ -456,6 +456,56 @@ async def test_search_router_returns_400_for_invalid_vector_query(
     assert "Vector retrieval requires a text query" in response.json()["detail"]
 
 
+@pytest.mark.asyncio
+async def test_semantic_search_uses_probe_pagination_without_count(
+    client: AsyncClient,
+    app,
+    v2_project_url: str,
+):
+    """Semantic searches should not run an extra count query."""
+    now = datetime.now(timezone.utc)
+    fake_rows = [
+        SearchIndexRow(
+            project_id=1,
+            id=row_id,
+            type="entity",
+            file_path=f"notes/semantic-{row_id}.md",
+            created_at=now,
+            updated_at=now,
+            title=f"Semantic Result {row_id}",
+            permalink=f"notes/semantic-{row_id}",
+            score=1.0 - (row_id / 10),
+        )
+        for row_id in range(1, 4)
+    ]
+
+    class FakeSearchService:
+        async def search(self, query, *, limit, offset):
+            assert query.retrieval_mode.value == "vector"
+            assert limit == 3
+            assert offset == 0
+            return fake_rows
+
+        async def count(self, *args, **kwargs):
+            raise AssertionError("semantic search must not run count")
+
+    app.dependency_overrides[get_search_service_v2_external] = lambda: FakeSearchService()
+    try:
+        response = await client.post(
+            f"{v2_project_url}/search/",
+            json={"text": "semantic query", "retrieval_mode": "vector"},
+            params={"page": 1, "page_size": 2},
+        )
+    finally:
+        app.dependency_overrides.pop(get_search_service_v2_external, None)
+
+    assert response.status_code == 200
+    data = response.json()
+    assert data["total"] == 0
+    assert data["has_more"] is True
+    assert len(data["results"]) == 2
+
+
 @pytest.mark.asyncio
 async def test_search_has_more_when_more_results_exist(
     client: AsyncClient,
diff --git a/tests/repository/test_semantic_search_base.py b/tests/repository/test_semantic_search_base.py
index 6998d199..ba4e08ef 100644
--- a/tests/repository/test_semantic_search_base.py
+++ b/tests/repository/test_semantic_search_base.py
@@ -318,6 +318,25 @@ async def test_sqlite_hybrid_search_raises_disabled_error(search_repository):
         )
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("retrieval_mode", [SearchRetrievalMode.VECTOR, SearchRetrievalMode.HYBRID])
+async def test_count_rejects_semantic_modes_without_running_search(monkeypatch, retrieval_mode):
+    """Semantic counts must not materialize vector or hybrid retrieval."""
+    repo = _ConcreteRepo()
+    search_calls = []
+
+    async def fail_if_search_runs(**kwargs):
+        search_calls.append(kwargs)
+        return []
+
+    monkeypatch.setattr(repo, "search", fail_if_search_runs)
+
+    with pytest.raises(ValueError, match="Exact counts are only supported for full-text search"):
+        await repo.count(search_text="semantic query", retrieval_mode=retrieval_mode)
+
+    assert search_calls == []
+
+
 @pytest.mark.asyncio
 async def test_sync_entity_vectors_batch_flushes_at_configured_threshold(monkeypatch):
     """Batch sync should flush queued jobs at semantic_embedding_sync_batch_size boundaries."""