diff --git a/duplicate_checker_old.py b/duplicate_checker_old.py
index 555b1d6e..3f223628 100644
--- a/duplicate_checker_old.py
+++ b/duplicate_checker_old.py
@@ -104,6 +104,13 @@ def assess_serp_trust(company_name: str, url: str) -> str:
 
 # --- Similarity ---
 def calculate_similarity(mrec: dict, crec: dict, token_freq: Counter):
+    n1 = mrec.get('normalized_name','')
+    n2 = crec.get('normalized_name','')
+    
+    # NEU: Direkte Prämierung für exakten Namens-Match
+    if n1 and n1 == n2:
+        return 300, {'name': 100, 'exact_match': 1}
+
     # Domain (mit Gate)
     dom1 = mrec.get('normalized_domain','')
     dom2 = crec.get('normalized_domain','')
@@ -314,39 +321,61 @@ def main():
     for idx, mrow in match_df.to_dict('index').items():
         processed += 1
         name_disp = mrow.get('CRM Name','')
-        # Kandidatenwahl
-        candidates = []
-        used_block = ''
+        
+        # --- NEUE KANDIDATEN-SAMMELLOGIK ---
+        candidate_records = {} # Dict, um Duplikate zu vermeiden und Records zu speichern
+        used_blocks = []
+
+        # 1. Priorität: Exakter Namens-Match
+        mrec_norm_name = mrow.get('normalized_name')
+        if mrec_norm_name:
+            exact_matches = crm_df[crm_df['normalized_name'] == mrec_norm_name]
+            if not exact_matches.empty:
+                for _, record in exact_matches.to_dict('index').items():
+                    candidate_records[record['CRM Name']] = record
+                used_blocks.append('exact_name')
+
+        # 2. Domain-Match
         if mrow.get('normalized_domain') and mrow.get('domain_use_flag') == 1:
-            candidates = domain_index.get(mrow['normalized_domain'], [])
-            used_block = f"domain:{mrow['normalized_domain']}"
-        if not candidates:
-            rtok = choose_rarest_token(mrow.get('normalized_name',''), token_freq)
-            if rtok:
-                candidates = token_index.get(rtok, [])
-                used_block = f"token:{rtok}"
-        if not candidates:
-            # Prefilter über gesamte CRM-Liste (strenger + limitierter; erfordert Rarest-Token-Overlap)
+            domain_cands = domain_index.get(mrow['normalized_domain'], [])
+            if domain_cands:
+                for record in domain_cands:
+                    candidate_records[record['CRM Name']] = record
+                used_blocks.append('domain')
+
+        # 3. Rarest-Token-Match
+        rtok = choose_rarest_token(mrow.get('normalized_name',''), token_freq)
+        if rtok:
+            token_cands = token_index.get(rtok, [])
+            if token_cands:
+                for record in token_cands:
+                    candidate_records[record['CRM Name']] = record
+                used_blocks.append('token')
+        
+        # 4. Prefilter als Fallback, wenn wenige Kandidaten gefunden wurden
+        if len(candidate_records) < PREFILTER_LIMIT:
             pf = []
             n1 = mrow.get('normalized_name','')
             rtok = choose_rarest_token(n1, token_freq)
             clean1, toks1 = clean_name_for_scoring(n1)
             if clean1:
                 for r in crm_records:
+                    if r['CRM Name'] in candidate_records: continue # Nicht erneut prüfen
                     n2 = r.get('normalized_name','')
                     clean2, toks2 = clean_name_for_scoring(n2)
-                    if not clean2:
-                        continue
-                    if rtok and rtok not in toks2:
+                    if not clean2 or (rtok and rtok not in toks2):
                         continue
                     pr = fuzz.partial_ratio(clean1, clean2)
                     if pr >= PREFILTER_MIN_PARTIAL:
                         pf.append((pr, r))
             pf.sort(key=lambda x: x[0], reverse=True)
-            candidates = [r for _, r in pf[:PREFILTER_LIMIT]]
-            used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}"
+            for _, record in pf[:PREFILTER_LIMIT]:
+                candidate_records[record['CRM Name']] = record
+            if pf: used_blocks.append('prefilter')
+
+        candidates = list(candidate_records.values())
+        logger.info(f"Prüfe {processed}/{total}: '{name_disp}' -> {len(candidates)} Kandidaten (Blocks={','.join(used_blocks)})")
 
-        logger.info(f"Prüfe {processed}/{total}: '{name_disp}' -> {len(candidates)} Kandidaten (Block={used_block})")
         if not candidates:
             results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'})
             continue