Accurate in silico protein–ligand binding affinity prediction can substantially accelerate drug discovery pipelines by prioritizing compounds for experimental testing, a typically lengthy and costly process. Given the success of machine-learning and artificial intelligence approaches in areas such as computer vision and natural language processing in the last few years, there have been significant developments towards their application in structure-based potency prediction. In this chapter we summarize recent progress in this field, and we provide readers with a thorough introduction of the basic aspects to take into account when developing such models.