Retrieval-Augmented Generation (RAG) has proven effective for text-only question answering, yet expanding it to visually rich documents remains a challenge. Existing multimodal benchmarks, often derived from visual question answering (VQA) datasets, or large vision-language model (LVLM)-generated query-image pairs, which often contain underspecified questions that assume direct image access. To mitigate this issue, we propose a two-stage query rewriting framework that first generates OCR-based image descriptions and then reformulates queries into precise, retrieval-friendly forms under explicit constraints. Experiments show consistent improvements across dense, hybrid and multimodal retrieval paradigms, with the most pronounced gains in visual document retrieval – Hits@1 rises from 21.0% to 56.6% with VDocRetriever and further to 79.3% when OCR-based descriptions are incorporated. These results indicate that query rewriting, particularly when combined with multimodal fusion, provides a reliable and scalable solution to bridge underspecified queries and improve retrieval over visually rich documents.