The mechanism of action (MoA) of a compound describes the biol. interaction through which it produces a pharmacol. effect.Multiple data sources can be used for the purpose of predicting MoA, including compound structural information, and various assays, such as those based on cell morphol., transcriptomics and metabolomics.In the present study we explored the benefits and potential additive/synergistic effects of combining structural information, in the form of Morgan fingerprints, and morphol. information, in the form of five-channel Cell Painting image data.For a set of 10 well represented MoA classes, we compared the performance of deep learning models trained on the two datasets sep. vs. a model trained on both datasets simultaneously.On a held-out test set we obtained a macro-averaged F1 score of 0.58 when training on only the structural data, 0.81 when training on only the image data, and 0.92 when training on both together.Thus indicating clear additive/synergistic effects and highlighting the benefit of integrating multiple data sources for MoA prediction.