While augmenting the dataset with multiple resonance structures does improve consistency and performance, it is important to note that its implementation can be challenging, depending on the types of chemical species in the dataset. This process may require meticulously modifying existing algorithms or extending them by incorporating additional templates and filters to obtain a representative set of valid resonance structures, particularly for species with elements beyond C, H, O, and N. For example, the resonance generation algorithm used in this work is inadequate for species with a net charge, such as ions. Even if generating resonance forms were assumed universally feasible, the training costs could become significantly higher depending on the prevalence of resonance-active species in the dataset.