Engineered proteins provide clinically and industrially impactful molecules and utility within fundamental research, yet inefficiencies in discovering lead variants with new desired functionality, while maintaining stability, hinder progress. Improved function, which can result from a few strategic mutations, is fundamentally separate from discovering novel function, which often requires large leaps in sequence space. While a highly diverse combinatorial library covering immense sequence space would empower protein discovery, the ability to sample only a minor subset of sequence space and the typical destabilization of random mutations preclude this strategy. A balance must be reached. At library scale, compounding several destabilizing mutations renders many variants unable to properly fold and devoid of function. Broadly searching sequence space while reducing the level of destabilization may enhance evolution. We exemplify this balance with affibody, a three-helix bundle protein scaffold. Using natural ligand data sets, stability and structural computations, and deep sequencing of thousands of binding variants, a protein library was designed on a sitewise basis with a gradient of mutational levels across 29% of the protein. In direct competition of biased and uniform libraries, both with 1 × 109 variants, for discovery of 6 × 104 ligands (5 × 103 clusters) toward seven targets, biased amino acid frequency increased ligand discovery 13 ± 3-fold. Evolutionarily favorable amino acids, both globally and site-specifically, are further elucidated. The sitewise amino acid bias aids evolutionary discovery by reducing the level of mutant destabilization as evidenced by a midpoint of denaturation (62 ± 4 °C) 15 °C higher than that of unbiased mutants (47 ± 11 °C; p < 0.001). Sitewise diversification, identified by high-throughput evolution and rational library design, improves discovery efficiency.