Epigenetic drug discovery is increasingly important across different therapeutic areas. Despite the large amount of SAR data stored in public data sets, that information has not been used on a large scale to develop predictive models that support the medicinal chemistry community's efforts working on these cutting-edge targets. To fill this gap, in this study, we developed and evaluated the performance of five state-of-the-art machine learning algorithms built on three molecular fingerprints of different designs to predict 55 epigenetic targets of small molecules. To the best of our knowledge, this is the first study covering epigenetic targets on a large-scale basis. The performance of the herein reported models was validated using two different approaches, involving their performance estimation for binary classifications in 10-fold cross-validations in the context of each target, as well as the performance of their combination in the epigenetic target prediction task evaluated over 10 balanced samples of compounds containing an equal number of known active compounds for each target.