Our primary goal was to test the representational robustness of SELFIES through variational auto-encoding. By seeking to minimize validity, we test SELFIES's 100% validity assumption and its robustness. Moreover, a large pool of invalid SELFIES strings would yield insights into the representation's failure modes and factors contributing to anomalous criteria. Ideally, we expected to observe a self-organized latent space that clustered high-validity and low-validity regions.