  We introduce MosAIc, an interactive web app that allows users to find pairs
of semantically related artworks that span different cultures, media, and
millennia. To create this application, we introduce Conditional Image Retrieval
(CIR) which combines visual similarity search with user supplied filters or
"conditions". This technique allows one to find pairs of similar images that
span distinct subsets of the image corpus. We provide a generic way to adapt
existing image retrieval data-structures to this new domain and provide
theoretical bounds on our approach's efficiency. To quantify the performance of
CIR systems, we introduce new datasets for evaluating CIR methods and show that
CIR performs non-parametric style transfer. Finally, we demonstrate that our
CIR data-structures can identify "blind spots" in Generative Adversarial
Networks (GAN) where they fail to properly model the true data distribution.
