During a drug discovery campaign, frequent questions from a medicinal chemist to a cheminformatician are: What should I test next? Can you suggest a diverse set of small molecules likely to bind to this protein? Which parts of the lead compound should I start modifying with what kind of functional groups to increase the binding affinity? Answering these kinds of questions involves multiple scientific observations, and thus, multiple computational steps. Potentially fruitful steps are individually addressed in talktorials T014-T017. Finally, in T018, all steps are combined in an automated pipeline that -starting with a protein structure and a lead or hit compound -proposes several similar ligands with optimized estimated affinities and interactions based on the docked protein-ligand structures. T014: Binding site detection. First, we need to know where ligands may bind to a protein of interest. Sometimes the binding site is known from experimental protein-ligand structures. If only experimental apo structures are available, putative binding sites can be predicted with computational methods. We demonstrate how to use the REST API of the ProteinsPlus webserver to detect the main pocket of an EGFR structure using the DoGSiteScorer pocket detection algorithm. To validate our results, the predicted pocket is compared with the KLIFS-defined kinase pocket, which encompasses 85 residues in contact with ligands in over 2000 kinase-ligand structures .