The SGD approach has been applied in computational catalysis 6 and materials science. 5e, 7 It starts with the generation of a pool of propositions (π), statements about the data that apply only to a portion of the data set. For the case of continuous candidate descriptive parameters, the propositions are inequalities constraining their values. Then, SGD identifies selectors (σ), i.e., statements formed by a number of propositions and the "AND" connector (denoted "∧"), that result in the selection of subgroups of materials and conditions with the most outstanding distributions of the target values with respect to the whole data set (Fig. ). The propositions entering these selectors can be seen as rules describing the exceptional SG behavior. The parameters entering these propositions are, in turn, the key, most relevant, descriptive parameters, out of all the offered ones, associated with the desired reactivity. Because the SG search is performed by maximizing a quality function that measures how outstanding specific subselections of data points are, this approach identifies a local behavior. Thus, the identified rules reflect the specific underlying processes resulting in outstanding performance. We apply the SGD-HTE-&-theory approach to the selective oxidation of propylene on SiO2-supported catalysts based on ruthenium, tungsten, and phosphorus. By using the product yield measured by HTE as target, we circumvent the need for the explicit modelling of the full catalytic progression. Additionally, because the candidate descriptive materials parameters can be calculated by first-principles methods, extensive materials characterization by experiment is not required and the resulting SG rules can be used to identify promising catalyst candidates which were not yet synthesized by experiment.