Predicting protein-ligand binding affinities is paramount for drug discovery, but remains one of the major challenges of computational chemistry. The binding free energy (ΔGbind) of non-covalent complexes relates directly with the thermodynamic equilibrium constant (Kd), and indirectly with the most common functional observables (Ki, IC50, and EC50) that are often taken as a proxy of binding affinity. The importance of ΔGbind has motivated the development of a wide spectrum of computational approaches to predict this property. Empirical (or trained) methods, also known as Quantitative Structure-Activity Relationship (QSAR) methods, rely on pre-existing data and have seen an important revival with the emergence of machine learning (ML) algorithms.