Prediction of compound‐protein interactions with fingerprints has recently become challenging in recent pharmaceutical science for an efficient drug discovery. We review two scalable methods for predicting drug‐protein interactions on fingerprints. Especially, we introduce two techniques of learning statistical models using lossless and lossy data compressions. The first one is a method using a trie representation of fingerprints which enables us to learn predictive models on the compressed format. The second one is a method using lossy data compression called feature maps (FMs). Recently, quite a few numbers of FMs for kernel approximations have been proposed and minwise hashing, one method of this kind. has been applied to predictions of compound‐protein interactions and shows an effectiveness of the method. Overall, we show learning statistical models on the compressed format is effective for predicting compound‐protein interactions on a large‐scale.