This study describes an efficient compound diversification strategy aimed at enhancing the potency of existing MAGL-inhibiting molecules (hits). We combine GNNbased C-H activation reaction prediction for in silico molecule library design with a suite of property prediction methods. These include structure-based scoring using template docking and prediction of key molecular properties such as lipophilicity, solubility, and permeability. Towards this end, a Minisci alkylation data set with reaction yield resolution was created, covering 13,490 reactions. Integrating a reaction prediction model trained on these data with different scoring methods ("machine learning funnel") enabled the computational generation of novel, synthetically accessible compound designs with improved physicochemical properties and potency. We applied this processing cascade to monoacylglycerol lipase (MAGL), a serine hydrolase crucial for the metabolism of the neuroprotective endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL represents a target for the development of selective inhibitors aimed at treating central nervous system (CNS) disorders associated with neuroinflammation . Using a selected set of MAGL-inhibiting hit structures that allow diversification through C-H Minisci-type alkylation in combination with a set of 211 carboxylic acids, we generated an enumerated data set of 26,375 in silico reaction products (Figure ). These virtual molecules were then subjected to the machine learning funnel, delivering 212 diversified, novel, synthetically accessible designs with predicted improved physicochemical properties and potency, of which 14 were synthesized and tested in a biochemical assay against the human, mouse, rat, and cynomolgus macaque MAGL enzyme.