Natural selection has created optimal catalysts that exhibit their convincing performance even with a number of sometimes counteracting constraints. Optimal performance of enzyme catalysis does not refer necessarily to maximum reaction rate. Rather, it may involve a compromise between specificity, rate, stability, and other chemical constraints ; in some cases, it may involve aintelligento control of rate and specificity. Because enzymes are capable of catalyzing reactions under mild conditions and with high substrate specificity that often is accompanied by high regioand enantioselectivity, it is not surprising that a continually increasing number of industrial and academic reports concern the use of enzyme catalysts in chemical synthesis as well as in biochemical and biomedical applications. However, the demands of modern synthesis and their commercial application were obviously not targeted during the natural evolution of enzymes. Considering a specific, nonnatural application, any property (or combination of properties) of an enzyme may therefore need to be improved. Of course, scientists desired to mimick nature's powerful concepts for tailoring specific enzymatic properties: Following pioneering experiments for evolving molecules in the test tube, evolutionary engineering of biomolecules was successfully realized with first selections of functional nucleic acids (ribozymes) by using the SELEX (systematic evolution of ligands by exponential enrichment with integrated optimization by non-linear analysis) procedure, 8] and with the development of high-affinity ligands (aptamers) by using similar techniques. Meanwhile, evolutionary engineering, also termed adirected evolutiono, has emerged as a key technology for biomolecular engineering and generated impressive results in the functional adaptation of enzymes to artificial environments. Certainly, evolution in the laboratory does not come to a halt at the optimization of single genes and proteins. Recent results excitingly demonstrate the success of amolecular breedingo of metabolic pathways, and even of complete genomes, and the end is not in sight yet. Directed evolution in the laboratory is highly attractive because its principles are simple and do not require detailed knowledge of structure, function, or mechanism. Essentially like natural evolution, directed evolution comprises the iterative implementation of (1) the generation of a alibraryo of mutated genes, (2) its functional expression, and (3) a sensitive assay to identify individuals showing the desired properties, either by selection or by screening (Figure 1). After each round, the genes of improved variants are deciphered and subsequently serve as parents for another round of optimization. This review covers the most important aspects of directed evolution and summarizes key solutions to problems of optimizing and understanding enzyme function.