Art is one of the few languages which transcends barriers of country, culture, and time. We aim to create an algorithm that can help discover the common semantic elements of art even between any culture, media, artist, or collection within the combined artworks of The Metropolitan Museum of Art and The Rijksmusem. Image retrieval systems allow individuals to find images that are semantically similar to a query image. This serves as the backbone of reverse image search engines and many product recommendation engines. We present a novel method for specializing image retrieval systems called conditional image retrieval. When applied over large art datasets, conditional image retrieval provides visual analogies that bring to light hidden connections among different artists, cultures, and media. Conditional image retrieval systems can efficiently find shared semantics between works of vastly different media and cultural origin. Our paper introduces new variants of K-Nearest Neighbor algorithms that support specializing to particular subsets of image collections on the fly.