Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to design novel ligands for protein targets of interest. This review summarizes the current state of the art in this field, emphasizing the recent development of deep neural networks for predicting protein-ligand binding. We explain the major technical challenges that have caused difficulty with predicting novel ligands, including the problems of sampling noise and the challenge of using benchmark datasets that are sufficiently unbiased that they allow the model to extrapolate to new regimes.