This study explores the integration of Large Language Model (LLM) and Information Retrieval (IR) components to enable filtered search over multimodal structured data. We identify core integration challenges and introduce a conceptual framework based on two paradigms: filtered retrieval and filtered re-ranking. With the focus on the latter, we employ RT-3 rank transformation to dynamically adjust the ranking scores according to user-defined filters across multiple attribute modalities – numerical, categorical, binary, and spatial. This approach avoids redundant computations, provides fine-grained control over modality prioritization, and mitigates over-constrained filtering via soft ranking. We implement our method in a publicly available web prototype using two real-world datasets, demonstrating its effectiveness in balancing semantic and contextual relevance while empirically validating improved system efficiency in multimodal search scenarios.