Abstract. We present a new image search technique that, given a background image, returns compatible foreground objects for image compositing tasks. The compatibility of a foreground object and a background scene depends on various aspects such as semantics, surrounding context, geometry, style and color. However, existing image search techniques measure the similarities on only a few aspects, and may return many results that are not suitable for compositing. Moreover, the importance of each factor may vary for different object categories and image content, making it difficult to manually define the matching criteria. In this paper, we propose to learn feature representations for foreground objects and background scenes respectively, where image content and object category information are jointly encoded during training. As a result, the learned features can adaptively encode the most important compatibility factors. We project the features to a common embedding space, so that the compatibility scores can be easily measured using the cosine similarity, enabling very efficient search. We collect an evaluation set consisting of eight object categories commonly used in compositing tasks, on which we demonstrate that our approach significantly outperforms other search techniques