We present a comparative analysis of dense retrievers and retrieval strategies for multi-turn conversational retrieval in SemEval-2026 Task 8 (MTRAGEval). Our official submission employed a fine-tuned E5-based dense retriever (E5-FT, ~110M parameters) with Hypothetical Document Embeddings (HyDE), achieving nDCG@5 of .3309, ranking 31 out of 38 systems. On the development set we also compared E5-FT versus BGE embeddings, dense-only versus hybrid retrieval strategies, and HyDE versus keyword extraction approaches. We found: (1) BGE (general-purpose, ~110M) outperforms our domain-fine-tuned E5-FT (~110M) by 30.5% on baseline retrieval, suggesting that model selection may matter more than domain-specific fine-tuning, (2) hybrid retrieval combining BM25 and dense methods provides complementary signals, with HyDE improving BM25 by 26.7% and dense retrieval by 4.0%, and (3) keyword-based query simplification degrades performance by 11-28% across domains, validating HyDE’s approach of preserving semantic richness through passage-level text.