Chemical methods such as directed evolution and some forms of the SELEX procedure implement evolutionary algorithms directly in vitro. They have a wide range of applications in detecting and targeting diseases and potential applications in other areas as well [1]. However it is relatively difficult and expensive to carry out these processes (by comparison with evolutionary computation), so that the underlying theory has seen limited development. For more complex problems, where multiple and dynamic objectives are involved, there is potential for substantial improvement in the search protocols. Simulation through the methods of evolutionary computation is one potential way to gain the necessary insights. The complex fitness functions and huge populations involved in combinatorial chemistry render detailed simulation infeasible. However detailed simulation is not needed, so long as simulations are sufficiently similar to yield qualitative insights. In this paper, we investigate whether one class of problems — those involving short-chain evolution, where stereochemical effects do not dominate — are likely to have sufficiently similar fitness landscapes to a simple problem, string matching, for useful inferences to be made. In the outcome, it appears that the differences between more detailed simulations and string matching are not sufficient to significantly alter the behaviour of evolutionary algorithms, so that string matching could be used as a realistic surrogate. This is valuable, because string matching can be implemented in GPUs, offering speed-ups to the level where populations of 107, or even 108, might be feasible, thus reducing the population gap between chemical and computer evolution.