Exquisitor is an interactive system that supports search and exploration in large multimedia collections by integrating conversational search with relevance feedback (RF). However, combining these approaches introduces challenges, including reconciling user expectations with system capabilities, mitigating over-reliance on text-based queries when RF may be more effective, and bridging feedback modalities across conversational and RF paradigms. This work proposes extensions to Exquisitor that leverage foundation models for query expansion, reformulation and refinement. By transparently adjusting user-submitted text queries in real-time, these extensions aim to enhance search effectiveness and improve the overall user experience.