Graphs and strings including SMILES, SELFIES, and DeepSMILES 13 can represent molecular data. However, these representations may contain edge cases that fail to map to the corresponding molecular data correctly. In addition to synthetic training set curation by generating (semantic) anomalies, such a scenario also presents an application domain for generating (representational) anomalies to support representation development efforts. In this work, we investigate the application of machine learning for representational anomaly generation to explore failure modes in a molecular string representation (SELFIES) and consequently test representational robustness. We also compare and contrast ML-based anomaly generation against baseline generative null models.