In this work, we propose a method blending representation learning and molecular docking to predict protein ligand interaction, a key building block of drug repurposing and discovery. Using Leishmaniasis as a case study, we analyze the speed-accuracy trade-off that representation learning methods provide when compared to more computationally intensive molecular docking methods. We find that while deep learning methods substantially reduce the screening burden for molecular docking by a factor of 600, they can not be trusted to find the top ligands binding to a given target. This suggests that current deep learning methods can be used to come up with a short list of most promising ligands but the final predictions should rely on molecular docking.