Three key points emerged from our analysis on comparing popular fingerprints. First, hashed-based fingerprints result in lower predictive errors on the test sets than the substructure keys-based fingerprints. However, hashed-based fingerprints have one main disadvantage compared to the substructure keys-based fingerprints, which is not having a one-to-one correspondence between the fingerprint vector and the chemical structure. This is not the case with the substructure keys-based fingerprints, where each bit corresponds to a predetermined substructure. This disadvantage does not affect our framework since we do not go back-and-forth between the fingerprint and the chemical structure, and only use the fingerprinting when evaluating the fitness function in the GA framework. Not performing crossover and mutation functions on the fingerprints makes it possible to overcome this main disadvantage associated with hashed-based fingerprints. Next, we note that the top performing polymer candidates identified within this framework does not depend on which fingerprint is used in the GA. The relative strength of the polymers with each other are similar with the two different fingerprints. The main difference with using different fingerprints is the absolute value of the fitness function, since the ML models trained on the substructure keys-based fingerprints tend to underestimate the gas permeability. Finally, using the proper descriptor for a given application is still an open question in the polymer informatics field, but we have shown that most often it does not affect the final result. However, we emphasize that more sophisticated descriptors, like physical descriptor vectors that include bulk properties of the polymer membranes, may make a difference in the top performing polymers identified from the GA . For example, we believe the glass transition temperature is a key property for polymer membranes, and including this information in the fingerprint can lead to better performing polymer membranes within our framework.