Invalid SELFIES strings contained at least one troublesome token, but valid SELFIES may contain them as well (Table , Table ). The troublesome tokens only invalidate a SELFIES string based on neighboring tokens' associated valence and characteristics. Accordingly, editing the token itself or adjusting neighboring tokens' valence/charge or atomic We investigated the VAE as a representational anomaly generator (i.e., as a smart generative black-box fuzzer) by decoding SELFIES strings as a function of the latent generative radius and assessing the strings for validity. We also mapped validity vs. radius trends evaluated for incompletely-trained models at epochs 0 (untrained VAE initialized using Glorotuniform scheme 54 ), 1, 5, and 7 (Figure ). These illustrate the VAE's growing ability to discriminate and cluster SELFIES by validity over the course of training. Models at epochs 0 and 1 fall short of the best null model (naive random) at minimizing validity, while models at epochs 5, 7, and 15 (final convergence) outperform the null model at varying radial domains.