We generated invalid SELFIES by radially surveying the VAE latent space. We randomly sampled points over 196-dimensional zero-centered hyper-sphere surfaces and decoded them to SELFIES strings. We assessed fixed-size generated SELFIES sets for their validity percentage as a function of their generative (decoding) radius. To assess their validity, we converted SELFIES to SMILES and tested the SMILES's convertibility to molecular graphs using RDKit. For instance, if the SMILES violated a valence rule, RDKit raised an error identifying the atom and count by which the atom's explicit valence exceeded maximum permission. To ensure consistency and as a secondary check, we likewise assessed SELF-IES validity using ChemWriter 1 and Smival 2 (Figure ) along with manual inspection for selected strings (Figure , "Default Constraints"). This testing approach abides by the SELFIES definition of validity, which rests on its modules' converted SMILES complying with standard molecular rules.