AbstractDirected evolution is a broadly successful strategy for protein engineering in the quest to enhance the stereoselectivity, activity, and thermostability of enzymes. To increase the efficiency of directed evolution based on iterative saturation mutagenesis, the adaptive substituent reordering algorithm (ASRA) is introduced here as an alternative to traditional quantitative structure–activity relationship (QSAR) methods for identifying potential protein mutants with desired properties from minimal sampling of focused libraries. The operation of ASRA depends on identifying the underlying regularity of the protein property landscape, allowing it to make predictions without explicit knowledge of the structure–property relationships. In a proof‐of‐principle study, ASRA identified all or most of the best enantioselective mutants among the synthesized epoxide hydrolase from Aspergillus niger, in the absence of peptide seeds with high E‐values. ASRA even revealed a laboratory error from irregularities of the reordered E‐value landscape alone.