Protein kinases remain among the most versatile and prospective therapeutic drug targets with currently 15 distinct compounds approved for use in humans and numerous clinical development programs. The vast majority of kinase inhibitors bind at the ATP site. Here we present an integrated workflow to amplify the rapidly increasing space of structurally resolved small molecule kinase ligands to generate novel inhibitors. Our approach considers both receptor-based similarity constraints in cocomplexes and ligand-based filtering/refinement methods to generate novel, drug-like matter. After building a comprehensive database of the structural kinome and identifying ATP-competitive ligands, we leverage local site similarities and site alignments to shuffle ligand fragments across the kinome. After extensive curation and standardization, our automated protocol starting from 936 cocrystal ATP-competitive binding sites generated about 150,000 new ligand structures among them over 26,000 lead-/drug-like compounds; the majority of those are novel based on structural similarity and scaffolds. In a retrospective analysis we demonstrate that our protocol produced known potent kinase inhibitors and we show how docking can be applied to prioritize the most likely efficacious compounds. Our workflow emulates a common strategy in medicinal chemistry to identify and swap corresponding moieties from known inhibitors to generate novel and potent leads. Here, we systematize and automate this approach leveraging available knowledge covering the entire human Kinome.