The computational cost is determined by the number of fitness function calls (equal to the number of generations times the population size) times the cost for a single fitness evaluation. The latter depends crucially on the level of the surrogate method while the choice of the PES model (and thus the fitness function) is critical in order to reliably reflect experimental results. Thus compromises have to be made between cost and accuracy. EVOLVE is currently interfaced with several external programs (Gaussian, Amber, OpenMM 76 ) that can be used for local gradient-based optimizations at different levels of theory. Note that the modular structure of EVOLVE, and the use of the ASE library to interface with external codes, greatly facilitates the integration of new fitness evaluators. Finding an appropriate surrogate model for the PES in terms of speed and accuracy for the relative energetics is investigated in Section 4.1. For the GA applications envisioned here, it is not absolutely necessary to reproduce the PES in every detail but for a given surrogate model to be satisfactory, it has to be able to drive the GA optimizations towards regions with a promising set of candidates also likely to belong to low-energy regions at the higher-level reference method.