At the University of Utah's Department of Chemistry, the Sigman Lab is searching for "hotspots," from experiments involving organic compounds and hundreds machine-learning generated descriptors of these compounds. A "hotspot" may be described as densely concentrated target reactions resulting from experimental asymmetric catalysis. In this project, we attempt to identify meaningful 2-dimensional feature spaces containing these hotspots. Using density measures, we score the "clustering" of highly reactive outcomes and their variance from all other outcomes within each pairwise combination of descriptors. The meaningfulness of the score is then determined using a permutation method which calculates whether a hotspot's distribution differs significantly from the overall distribution of outcomes in its respective 2-dimensional feature space.