Retrieving relevant pages from visually rich documents, with evidence in charts, tables, and figures rather than body text, remains difficult: a single-pass query against a fixed index misses content in visual elements, and reordering the same candidates cannot recover a page never retrieved. We propose Reflect and Retrieve, an agentic retrieval approach centered on a Reflection Agent. After an initial page-level retrieval, the agent uses a multimodal large language model to visually reason over the retrieved page images, judge whether the evidence is sufficient, and, when gaps remain, an orchestrator issues targeted follow-up retrievals to recover the missing pages. Evaluating on two benchmarks, MMDocIR and ViDoRe V3, the Reflection Agent delivers the dominant agentic gain: it is the only configuration to beat single-step retrieval for every reasoning model on both, improving Recall@N from 0.628 to 0.663 on MMDocIR and 0.566 to 0.644 on ViDoRe V3. The improvement comes from recovering relevant pages absent in initial retrieval. Our approach indicates a clear design order: selecting an optimal embedding approach for the retrieval tool is the first step, after which the Reflection Agent contributes more value than a query decomposition agent, positioning visual reflection as the key component for document search.