Use of FuncLib allows us to consider the effect of mutations at 11 positions simultaneously, thus avoiding problems caused by epistasis which can lead to unpredictable (non-additive) effects on enzyme activity. Remarkably, we find that screening of just 20 FuncLib predicted variants leads to substantial enhancement of our previous best Kemp eliminase. That is, experimental validation of the twenty best scoring FuncLib predictions through biochemical and structural analysis allows us to identify 4 variants with significantly enhanced catalytic efficiency and improved turnover number, the best of which reach catalysis levels (kcat/KM of ~2•10 4 M -1 s -1 and kcat of ~10 2 s -1 ) for the cleavage of 5-nitrobenzisoxazole that compare favorably with that of naturally occurring enzymes. In addition, we demonstrate that the empirical valence bond (EVB) can reproduce the experimental free energy barriers for the optimized eliminases to within ~2 kcal•mol -1 , raising the possibility of further enhancing the stability-guidance of FuncLib on the basis of EVB-based computational predictions of catalytic activity. Overall, we demonstrate a simple computational protocol with tremendous potential for biocatalysis.