The practical need for highly efficient enzymes presents new challenges in enzyme engineering, in particular, the need to improve catalytic turnover (kcat) or efficiency (kcat/KM) by several orders of magnitude. However, optimizing catalysis demands navigation through complex and rugged fitness landscapes, with optimization trajectories often leading to strong diminishing returns and dead-ends. When no further improvements are observed in library screens or selections, it remains unclear whether the maximal catalytic efficiency of the enzyme (the catalytic 'fitness peak') has been reached; or perhaps, an alternative combination of mutations exists that could yield additional improvements. Here, we discuss fundamental aspects of the process of catalytic optimization, and offer practical solutions with respect to overcoming optimization plateaus.