Machine learning methods have proven to be useful in multiple areas of drug discovery, one such being target prediction of small molecules. For instance, in a retrospective large-scale comparison of machine learning methods for target prediction on ChEMBL (in the context of biochemical assays), deep neural networks were the best performing method for this task when trained on Extended Connectivity Fingerprints 37 (ECFP) of chemical compounds. However, the application of machine learning models for large-scale epigenetic target prediction has been explored on a limited basis, with most works focused on single targets or protein families such as HDACs or the BET family. Herein, we aimed to develop accurate models for epigenetic target prediction based on stateof-the-art machine learning algorithms trained on different fingerprint representation of compounds. We describe the development of predictive models with high precision for 55 epigenetic targets. Derivation of such predictive models is relevant for medicinal chemistry to develop hypothesis for the discovery of new epigenetic probes and drugs. The best models herein generated are implemented in an easy-to-use web application freely available to support medicinal chemistry projects related to epigenetic drug and probe discovery. It is anticipated that this tool will assist epigenetic drug design and development projects in the design and selection of compounds with potential epigenetic activity.