This study presents a comprehensive machine learningbased workflow that integrates binding potency predictions, reaction forward prediction models, and AD-MET property evaluation to rapidly select, prioritize, and synthesize novel chemical entities with strongly enhanced inhibitory activity. The workflow accelerates early hit-to-lead optimization by rapidly diversifying and evaluating new molecules, thereby reducing both cycle times and material consumption. It provides efficient access to novel chemical space by reducing the number of design-synthesize-test cycles required for the identification of new chemical matter. This process is adaptable to other projects and reaction types. The success of this approach hinges on the availability of high-quality, well-curated data sets for machine learning and access to reliable binding models for templatebased docking and affinity prediction. This study advocates for the adoption of FAIR principles (Findable, Accessible, Interoperable, Reusable) in data collection across the drug discovery cycle, from synthesis to biological evaluation. Such efforts will enable machine learning cascades to further streamline and accelerate hit-to-lead optimization in medicinal chemistry.