Among the initial products (P100-P111) assayed, it was apparent that a substantial bias existed in the yield distribution with a ~12:1 ratio favoring off reactivity over on reactivity (Fig. ). Notably, we found that ligands L19-L24 were the only ligands to produce yields exceeding 20%. This observation led us to hypothesize that these ligands must share common structural features that enhance their activity in Ullmann couplings under the conditions employed. Therefore, we submitted this dataset to a classification algorithm (single node decision tree) to identify if they share structural features (see computational details in the SI) . This algorithm searches for ligand structural descriptors that provide the most accurate distinction between active and inactive ligands. Indeed, the findings revealed that the ligands could be effectively binned into two groups according to the computed Cu-Ligand interaction distances (d), with high classification accuracy (see SI for all model metrics). Ligands that produced yields exceeding 20% all displayed computed interaction distances measuring less than 2.07Å (Fig. , with only L12 being misclassified). The computed distances could be indicative of both the stability of the Cu-L complex in solution as well as its capacity to engage in the catalytic cycle . It is worth noting that all ligands with optimal computed distance are anionic in nature, which could potentially play a pivotal role in the effectiveness of these ligands in Ullmann couplings, as recently suggested by Buchwald and Hartwig . To leverage this discovery, we adopted an active learning strategy to resample other commercial ligands, identifying 12 examples (L25-L36) that were predicted to be catalytically active by the classification model (i.e., ligands displaying optimal computed distances). Experimentally, all the newly selected ligands were found to surpass 20% maximum yield, validating the classification model. More importantly, the newly introduced ligands produced a better-balanced yield distribution across the 12 initial products, ~1:1 ratio between instances of on and off reactivity (Fig. ). These results showcase the efficacy of active learning to enhance dataset representation .