The fundamental challenge of RO lies in its expansive search space, which consists of a multitude of categorical (e.g., choice of reagent, base, catalyst, methods) and continuous (e.g., temperature, concentration, reagent equivalent) parameters. Some common approaches to solving such a task are high-throughput experimentation (HTE) , one factor at a time (OFAT) , design of experiment (DoE) , or, more recently, AI-guided optimization . All approaches have the ultimate goal in common to identify optimal solution(s) within the high-dimensional parameter space.