We describe our SemEval-2026 Task 8 Subtask A system, which focuses on evaluating and improving the retrieval aspect of multi-turn Retrieval-Augmented Generation (RAG) conversations. We implement a training-free fusion approach that combines three distinct query representations to retrieve documents independently. The results from these three views are pooled and reranked using a MonoT5 cross-encoder. Our findings demonstrate that this fusion approach consistently outperforms single-strategy baselines, revealing that optimal retrieval strategies vary significantly at the query level, and establishing multi-query fusion as a baseline for multi-turn RAG systems.