This remarkable feat can be explained as follows: 1-(26/27) 39 or 77% of the 6.7×10 55 possible sequences have at least one correctly placed character compared to the target sequence (Eq S1). This means that for an initial mating pool of 100 random sequences, an average of 77 sequences will have a score of at least 1. An average 38±1 of the 39 positions are correctly represented in at least one gene (Eqs S4-S5). Since the score is additive, it is very likely that a crossover will result in a child with a higher score. Indeed simulations show that the tends score increases by 1 with every generation until the score reaches about 20, i.e. until about half the letters are correctly placed. This makes sense because, on average, each parent contributes half the genetic information and the correctly placed letters are evenly distributed in the initial population. After half the letters and spaces are placed correctly, the score increases more slowly and it can take many generations to place the last character since that tends to occur solely through random mutations in the current GA implementation. So rather than picturing 6.7×10 55 random sequences that one must sift through, one should picture an enormous number of interconnected paths that connects low-scoring sequences to the target sequence (Figure ). Since 77% sequences have a score of at least 1, one is very likely to encounter such a path by chance and one can then follow the path to the target sequence using a search algorithm such as a GA. However, such paths only exist if the score increases in a relatively smooth fashion as one gets closer to the target. Figure shows plots similar to Figure , but where there the score only increases if the number of correctly placed characters increases by 2. After about half of the characters are placed correctly, it becomes less likely that a mating operation or mutation increases the score and none of 10 simulations manages to find the correct sequence in 1000 generations. If the score only increases if the number of correctly placed characters increases by 5, then the the GA fails to increase the score beyond 15 (Figure ). A chemical example of non-continuous scores is discussed below. )) and molecular representation (graph, SMILES, DeepSMILES, and SELFIES). The score is the Tanimoto similarity to the target molecule computed using ECFP4 circular fingerprints. The population size is 100, so up to 100,000 different molecules are evaluated in 1000 generations. For each plot we show the success rate and the median number of generations for successful runs.