In one of the the three rediscovery targets (troglitazone) the success rate can be improved by using DeepSMILES, a string-based molecular representation with simpler syntax compared to SMILES. It is also quite likely that the success can be improved further by more sophisticated mating a mutation operations designed specifically for particular syntax associated with each molecular representation. The success rate can be improved by performing mating and mutation operations directly in the molecular graph (i.e. the atom and bonds, Figure and), where a particular molecule can be rediscovered with >99% certainty by evaluating between 210,000 and 1.7 million (10 5-6 ) molecules -a very small fraction of chemical space. In analogy with the simple phrase example, the reason is that the chemical "alphabet" of organic chemistry is relatively small ca ten different atoms and three different bonds. So it is quite likely that a randomly chosen molecule has something in common (e.g. a C-C bond or a pyridine ring) with the target molecule and thus lies on a path that a search algorithm can follow to the target (Figure ). For example, more than 99.9% of the 1.6 million molecules in the ChEMBL data set, used to construct the initial populations, have a non-zero Tanimoto similarity with the three rediscovery targets (Figure ). Most drug-like molecules have at most 50 atoms and bonds, so the number of changes needed to inter-convert two very different molecules (the so called graph edit distance) is generally less than 100. So, while chemical space is vast, the ideal search algorithm can traverse it very quickly -as long as the desired property changes incrementally.