Although scoring functions such as SFCscore , NNScore 2.0 20 , and those used in AutoDock 4 14 and AutoDock Vina 15 include some features of the ligand, the use of detailed information about the ligand to predict cognate protein-ligand binding affinity remains limited. Perhaps the closest is the field of proteochemometric modeling, in which classification models are constructed using a combination of features describing the protein and the small molecule. This approach has proven to be capable of predicting ligand selectivity 27 and capturing polypharmacology. Motivated by the utility of ligand-based features in virtual screening and proteochemometric modelling, we investigated whether a more detailed representation of the ligand can improve the ability of a scoring function to predict its binding affinity. Using the cheminformatics toolkit RDKit (, accessed 17/05/2019) we computed a diverse set of 1D and 2D ligand molecular descriptors and combined these with the structure-based features used by the machine learning scoring functions RF-Score 19 , RF-Score v3 29 , NNScore 2.0 20 , as well as the empirical scoring function of AutoDock Vina 15 . We show that a Random Forest (RF) regression model using both structure-based and ligand-based features consistently outperforms a model using only structure-based features when benchmarked on three versions of the PDBbind 31 core set, corresponding to the scoring power test of the three Comparative Assessment of Scoring Functions 17, .