High-throughput experimentation (HTE) has emerged as a time-and material efficient technique for producing large amounts of chemical reaction data . HTE is thus a reasonable approach to yield datasets for CASP, which requires large training sets. Recent developments in HTE, and automation in general, have enabled platforms that can conduct and analyse thousands of experiments per day as demonstrated for batch chemistry as well as for flow chemistry . However, it is not feasible to use HTE to investigate all necessary permutations of reaction variables in a typical reaction . Therefore, it is important to identify the most informative data points, e.g., finding the smallest subset of data points that provides the most information about a reaction to a given machine learning model.