The theoretical reaction space generated by the substrates and conditions in focus encompassed nearly 115,000 possible combinations. To enable efficient evaluation of a relevant portion of this reaction space, a rapid and miniaturized HTE workflow was implemented (Figure ). Utilizing stock solutions and automated liquid handling, reaction plates were prepared in a reproducible and efficient manner. Reactions were analyzed via liquid chromatography-mass spectrometry (LC-MS) to determine the reaction outcome, employing an automated tagging workflow. In total, 13,490 reactions were conducted, representing approximately 12% of all possible combinations (Figure ). Data analysis revealed that 30% of the reactions resulted in positive reaction outcomes, i.e., successful alkylation of the heterocyclic fragment with a detectable reaction yield of ≥5% (Figure ). Among these successful reactions, a balanced yield distribution was observed (Figure ), making this generated data set suitable for training a machine learning model for reaction forward prediction.