Reliably operating LLMs in specialized domains often requires retrieval-augmented generation (RAG), which grounds the model’s responses in domain-specific information. Retrieval performance is critical for output quality, making retrieval model selection a key decision for specialized RAG deployments. To guide retrieval model selection, we ask whether domain-tuned encoders produce better-structured embedding spaces for specialized corpora than general-purpose encoders, and whether geometric regularity alone reliably indicates retrieval quality in these settings. Our results show that the answer is more nuanced than expected: domain-specific models show more pronounced geometric irregularity than general-domain encoders, while geometric regularity alone does not reliably indicate retrieval quality. Moreover, geometric and intervention patterns observed on MS-MARCO do not consistently transfer to specialized corpora.