Multimodal memes are dynamic and often require up-to-date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowledge of pre-trained models that may be incomplete, outdated, or unavailable for emerging memes. We introduce Query-Retrieve-Conclude, a zero-shot framework that identifies missing knowledge, retrieves open-web evidence, and synthesizes evidence-grounded background knowledge for meme understanding and detection. We also introduce a curated meme understanding benchmark of recent memes from 2024–2026 with external background knowledge annotations. Experiments on three meme understanding datasets and five meme detection tasks show that our framework improves knowledge recovery, meme understanding and downstream detection over zero-shot baselines.