We investigated the effect of doubling the maximum number of energy evaluations to 200 on the Bayesian optimization algorithm, for the set of molecules with five rotatable bonds. We found that the results were more robust and had smaller ranges of energetic differences than were found with 100 iterations. Fig. shows that the median of the energy difference distributions decreased by 1.1 kcal/mol for EI, and 1.3 kcal/mol for LCB. The maximum variation also decreased, by 1.5 kcal/mol for EI, and 1.7 kcal/mol for LCB. Thus, increasing the maximum number of iterations improves the likelihood of finding low-energy minima, and decreases the stochastic variance between multiple runs.