To demonstrate how these ML models could be used for HTVS, this study explores three scenarios for searching for catalysts and active sites. In each scenario, the strategies that use our ML models significantly reduce the number of necessary DFT calculations, thus greatly decreasing the computational cost and time required to screen for active catalysts. Of note, despite the loss of accuracy, the models trained only on structural features are effective for HTVS as long as the goal is to only identify a limited number of active sites. Although the structure-only classifier is less accurate, its accuracy suffers more from false negatives than false positives, and thus it remains useful because HTVS suffers more from false positives when the distribution contains sufficient active sites that at least some are still found. In future work, the strategies applied in these scenarios could be used to screen larger molecular libraries for promising GCCs. As the synthetic framework for creating these catalysts works for a range of chemical structures that contain diamines, a good starting point would be collecting a much larger library of diamines from such chemical libraries as ChemSpider. This library could be converted into virtual GCC structures, then screened by ML model trained on structural features, such as the one in this work, to select for promising candidates that are further screened with models that include DFT features. This process could readily be extended to other catalytic reactions that are suited for GCCs.