diff --git a/frigate/api/event.py b/frigate/api/event.py
index b1c98bb19..c716bba13 100644
--- a/frigate/api/event.py
+++ b/frigate/api/event.py
@@ -481,20 +481,15 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
     else:
         search_types = search_type.split(",")
 
-        # only normalize multi-modal searches
-        apply_normalization = (
-            "thumbnail" in search_types and "description" in search_types
-        )
+        # only save stats for multi-modal searches
+        save_stats = "thumbnail" in search_types and "description" in search_types
 
         if "thumbnail" in search_types:
             thumb_result = context.search_thumbnail(query)
 
-            if apply_normalization:
-                thumb_distances = context.thumb_stats.normalize(
-                    [result[1] for result in thumb_result]
-                )
-            else:
-                thumb_distances = [result[1] for result in thumb_result]
+            thumb_distances = context.thumb_stats.normalize(
+                [result[1] for result in thumb_result], save_stats
+            )
 
             thumb_ids = dict(
                 zip([result[0] for result in thumb_result], thumb_distances)
@@ -508,14 +503,10 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
 
         if "description" in search_types:
             desc_result = context.search_description(query)
-            if apply_normalization:
-                desc_distances = context.desc_stats.normalize(
-                    [result[1] for result in desc_result]
-                )
-            else:
-                desc_distances = [
-                    result[1] for result in desc_result
-                ]  # Use raw distances
+
+            desc_distances = context.desc_stats.normalize(
+                [result[1] for result in desc_result], save_stats
+            )
 
             desc_ids = dict(zip([result[0] for result in desc_result], desc_distances))
 
diff --git a/frigate/embeddings/util.py b/frigate/embeddings/util.py
index 3d7605057..bc1a952ec 100644
--- a/frigate/embeddings/util.py
+++ b/frigate/embeddings/util.py
@@ -22,8 +22,9 @@ class ZScoreNormalization:
     def stddev(self):
         return math.sqrt(self.variance) if self.variance > 0 else 0.0
 
-    def normalize(self, distances: list[float]):
-        self._update(distances)
+    def normalize(self, distances: list[float], save_stats: bool):
+        if save_stats:
+            self._update(distances)
         if self.stddev == 0:
             return distances
         return [
diff --git a/web/src/views/search/SearchView.tsx b/web/src/views/search/SearchView.tsx
index de1da0746..4c33f7dc8 100644
--- a/web/src/views/search/SearchView.tsx
+++ b/web/src/views/search/SearchView.tsx
@@ -190,17 +190,13 @@ export default function SearchView({
   // confidence score
 
   const zScoreToConfidence = (score: number) => {
-    // Normalizing is needed for multi-modal searches only
+    // Normalizing is not needed for similarity searches
     // Sigmoid function for normalized: 1 / (1 + e^x)
-    // Cosine for non-normalized
+    // Cosine for similarity
     if (searchFilter) {
-      const normalized =
-        !searchFilter.search_type ||
-        (Array.isArray(searchFilter.search_type) &&
-          searchFilter.search_type.length === 2 &&
-          searchFilter.search_type.includes("thumbnail") &&
-          searchFilter.search_type.includes("description"));
-      const confidence = normalized ? 1 / (1 + Math.exp(score)) : 1 - score;
+      const notNormalized = searchFilter?.search_type?.includes("similarity");
+
+      const confidence = notNormalized ? 1 - score : 1 / (1 + Math.exp(score));
 
       return Math.round(confidence * 100);
     }