Genetic algorithms, which are tools developed to enhance rational drug design through simplification, instead avoid rather than resolve the problems of complexity. Three examples are provided and described that bolster this point. The first involves cyclooxygenase enzyme isoforms 1 and 2, and the second points to the fact that complex systems do not produce optimal solutions and are not in all respects predictable as to future behavior. The third shows that success depends on hindsight (i.e., a carefully chosen fitness function). The three scenarios are useful in discussing the role of analysis in comprehension of complexity and the limits of prediction in biology. Rational drug design is impeded by the fact that researchers do not know which structures have biological activity or represent the global energy minimum simply by looking at them. They are to be found from a set of possible solutions. One way to find the best solution is to compute the whole search space and test all for the desired function. Genetic algorithms are used, because they can reduce the space, but they find suboptimal solutions. Nature tells uses that trial and error is the best strategy for finding novel solutions in complex organisms. A study is mentioned in which genetic algorithms were used for virtual screening for drug side effects through the searching of several targets for one ligand. The study supports the general strategy of using docking energy as fitness function to predict specificity of binding. The study also concludes that this prediction is only dependable for protein structures for which a higher resolution structure in the presence of a bound ligand already exists. Real progress can only result from implementation of biological function algorithms, not those founded only in structural data. The properties of complex systems are called emergent properties, and the system itself is considered to be irreducibly complex, so the limitations of complexity that are imposed on scientific inquiry should be accepted. A theory of homeostasis is needed to make significant progress in biology.