Experimental design includes five main steps 1 as depicted in Fig. : i) define the objective of the experiments, for instance, for a chemical reaction, the objective can be maximizing the yield of a desired product; ii) select the relevant variables and their design space. To address these challenges, different approaches have been studied and developed over the years to optimize the experimental planning process. For example, recent developments in high-throughput experimentation (HTE) have significantly broadened the scope of experimental capabilities, allowing for the collection of several thousand data points within a constrained set of conditions in a reasonable time frame. The process of HTE implies a substantial enhancement in the efficiency and scale of data acquisition compared to traditional experimental methods . Yet, deciding on the constrained set of conditions to test remains challenging. One proposed solution is to integrate machine learning methods with HTE to efficiently navigate the search space .