3, 4 Most applications of enzymes require their optimization in terms of the relevant biomolecular properties. Developing methodologies for enzyme enhancement is therefore a goal with major economic impact. However, while increasing enzyme activity is obviously desirable for many industrial applications, so is ensuring sufficient enzyme stability. That is, low stability may facilitate irreversible enzyme denaturation and thus limit the total turnover of the enzyme under the conditions of the application. It may also simply preclude the use of high temperatures in the process, which may be required to warrant sufficient substrate solubility and to prevent microbial growth or simply to lead to even higher catalytic rates. Unfortunately, most mutations that are capable of modulating enzyme function are destabilizing, which explains why activity-stability trade-offs are often found in enzyme optimization studies. Certainly, it is plausible that a few mutations (or combinations of mutations) modulate activity without impairing stability. However, these mutations (or combinations thereof) are unlikely to be found through the screening of large random variant libraries. While this can to some extent be offset by the use of laboratory directed evolution, which is the most useful and broadly applicable methodology for protein engineering, this process is typically sluggish, and many rounds of time-consuming library screening are often required to reach the required levels of the targeted biomolecular properties. These challenges are obviously a reflection of the fact that the protein sequence space is vast and that most sequences do not encode for proteins with industrially desired biophysical properties. In view of this, considerable effort has been devoted in recent years to the development of general methodologies to design small focused libraries for guided directed evolution. In the present work, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed through the combination of two recently proposed methodologies to guide directed evolution. Our combined approach involves, first, the use of NMR to determine positions in the protein sequence at which mutations are likely to affect catalysis (catalytic hotspots). In this context, recent work has demonstrated that such catalysis hotspots can be identified from the chemical shift perturbations induced by the binding of a transition-state analogue to the targeted enzyme. This is important, as focusing directed evolution to catalytic hotspots bypasses one of the main factors that contributes to the ineffective screening of large random libraries, namely, that only certain regions of the enzyme structure are linked to the catalytic cycle and, therefore, that the screening of a random library involves the useless and time-consuming probing of large numbers of mutations that in fact do not affect catalysis.