Arginases catalyze the divalent cation-dependent hydrolysis of l-arginine to urea and l-ornithine. There is significant interest in using arginase as a therapeutic anti-neogenic agent against l-arginine auxotrophic tumors and in enzyme replacement therapy for treating hyperargininemia. Both therapeutic applications require enzymes with sufficient stability under physiological conditions. To explore sequence elements that contribute to arginase stability we used SCHEMA-guided recombination to design a library of chimeric enzymes composed of sequence fragments from the two human isozymes Arginase I and II. We then developed a novel active learning algorithm that selects sequences from this library that are both highly informative and functional. Using high-throughput gene synthesis and our two-step active learning algorithm, we were able to rapidly create a small but highly informative set of seven enzymatically active chimeras that had an average variant distance of 40 mutations from the closest parent arginase. Within this set of sequences, linear regression was used to identify the sequence elements that contribute to the long-term stability of human arginase under physiological conditions. This approach revealed a striking correlation between the isoelectric point and the long-term stability of the enzyme to deactivation under physiological conditions.