Deep learning has enabled exciting new avenues in drug discovery. Despite the need for large training datasets being the 'Achilles heel' of deep learning in drug discovery, several advances allow neural networks to be, paradoxically, powerful tools in low-data scenarios. An increasing body of literature shows how strategies like the ones discussed in this minireview can lead to highperforming deep learning models, even with little data. However, many challenges are still lurking in the data shallows.