A current 'known unknown' in the field is the minimal data requirement for deep learning in drug discovery. Only a limited number of studies systematically examine the effect of dataset size and diversity on the model performance and out-of-domain generalization , , . The same holds for knowledge on what deep learning strategy to choose based on the task and data at hand. In this context, FSmol -which provides the first-in-kind benchmark and set of baselines for low-data training -is a notable effort to further propel deep learning approaches in drug discovery. We expect the development of metrics and datasets tailored to low-data training to be key to harmonize the evaluation, choice, and development of novel approaches with an increased potential for drug discovery.