The point of stopping after 4 or 5 iterations and then restarting with a ranked list is to improve the rate of population improvement towards a local minima. This is a typical maneuver in using genetic algorithms if the fitness function is very complicated and the time of computation required very large. A population of 30 chromosomes is sufficient to obtain results in 33 iterations. A typical iteration requires about 4 hours with 64 cores. Genetic algorithms are known to converge rapidly in less than 15 iterations, very much depending on the complexity of the fitness function and size of the search space, and much more iterations are necessary to improve accuracy at the minima, i.e. flattening off behavior of genetic algorithm best scores. It should be noted that due to the non-zero elite_fraction in the GA, a portion of the non-isomeric molecules is resampled and docked by GOLD, and this improves the accuracy of the calculation while others are kept after the 4 or 5 iterations.