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Deploying speaker retrieval systems requires robust cross-domain embedding generalization. However, existing benchmarks focus on verification metrics, leaving ranking stability under retrieval constraints under-explored. This paper evaluates six pre-trained embedding models across multiple cross-domain scenarios. First, while supervised multi-scale models resist channel filtering and aging drift, most architectures overfit to language-specific phonetic variations under cross-lingual mismatch. Second, we leverage adaptive symmetric normalization as a training-free backend strategy to improve retrieval performance. By selecting high-scoring background cohorts to estimate localized score statistics, this strategy normalizes global shifts caused by channel distortions and restores ranking consistency across all models. These insights demonstrate that combining multi-scale local features with adaptive backend calibration is effective for cross-domain speaker retrieval.