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With the explosive growth of social media applications on the internet, billions of social images have been made available in many social media web sites nowadays. This has presented an open challenge of web-scale social image search. Unlike existing commercial web search engines that often adopt text based retrieval, in this demo, we present a novel web-based multimodal paradigm for large-scale social image retrieval, termed "Social Image Retrieval Engine" (SIRE), which effectively exploits both textual and visual contents to narrow down the semantic gap between high-level concepts and low-level visual features. A relevance feedback mechanism is also equipped to learn with user's feedback to refine the search results interactively. Our live demo is available at http://msm.cais.ntu.edu.sg/SIRE, and a video is available athttp://www.youtube.com/user/msmntu.