). This procedure yielded SELFIES with a maximum token count of 91 (Table , Figure ). We trained the model using a sparse softmax cross entropy with logits loss (for reconstruction), KL divergence (for regularization), and Adam optimizer. We initialized parameters using a Glorot-uniform scheme and trained with a batch size of 128. We annealed the β-VAE 52 's β factor (KL loss weight) using a linear schedule after three epochs, with the final model trained in 15 epochs over 74 hours (wall clock time) on a single NVIDIA RTX 3090 GPU. The annealing process dynamically updates β throughout training to mitigate KL vanishing and posterior collapse tendency. We used early stopping with a patience of 8 to determine convergence. We used the RDKit library for cheminformatics functions (RDKit: Open-source cheminformatics; ). Setting Null Baselines for Deep Variational Anomaly Generation ML methods may not be justified if they do not outperform simpler conventional algorithms.