At first impression our results indicate that our graph-based GA is able to find a specific molecule in chemical space by evaluating only a very small subset of between ≤35,400 molecules (troglitazone) and ≤1,000,000 molecules (tiotixene). Troglitazone is rediscovered with a 100% certainty in 354 generations or less, where 100 molecules is evaluated for each generation. Tiotixene is rediscovered successfully in 38% of the GA searches, meaning that a minimum of 10 GA searches have to be performed to rediscover tiotixene with a 99% certainty, where each search requires up to 100,000 molecules to be evaluated. However, the initial mating pool was constructed following Brown et al. , i.e. the 100 top-scoring molecules in a 1.6 million molecule ChEMBL subset, where molecules with an ECFP4 Tanimoto similarity of >0.323 are removed. So constructing the initial population itself requires 1.6 million molecules to be evaluated and this "cost" must be added. If instead the initial population is constructed as before but from 10,000 molecules chosen from the 1.6 million ChEMBL subset, the success rates are 93%, 70%, and 25%, respectively, meaning that at least 2, 4, and 17 GA searches are needed for rediscovery to succeed with >99% certainty (Figure ). Thus, between 210,000 and 1,710,000 molecules need to be evaluated to find one particular molecule in chemical space using our GA. All the fragments in celecoxib and troglitazone are in the respective initial population, while two fragments are missing for tiotixene (Figure ). This indicates the substructures that make up the three molecules are relatively common in the ChEMBL data set.