The other major limitation of using genetic algorithms in molecular optimisation stems from the inherent randomness of the molecules generated by mutations and crossovers. A genetic algorithm does not explicitly make use of information about the fitness of molecules encountered during its optimisation run. This limitation hinders the algorithm's ability to optimally make use of its exploration of chemical space and slows down its convergence towards the desired molecules. The information generated by genetic algorithms could be exploited to prioritise the evaluation of promising molecules. A potential approach to use this information is to integrate Bayesian optimisation (based on a surrogate fitness model and an appropriate acquisition function) with the genetic algorithm to determine which new molecules are selected for fitness evaluation.