To establish whether SMILES correction could yield novel, complementary molecules, the fixed molecules were analyzed and compared to readily generated molecules from the three general generative models and the training set. 97 % of the molecules generated by the SMILES corrector were unique. Interestingly, more than 97 % of the molecules were novel compared to the readily generated molecules and the average SNN between the two sets was relatively low (0.45). This indicates that SMILES correction was able to generate compounds that were novel and dissimilar compared to the ones already generated. The readily generated and fixed molecules did not differ in terms of their novelty and similarity compared to the training set. Additionally, both the fixed molecules and readily generated ones approximated the training set to a similar extent in regards to their property distributions. These findings suggest that SMILES correction generates molecules that are as useful as the molecules created by the generator that the errors originated from.