We train and analyze a SELFIES-VAE in TensorFlow v. 2.10.0 47 with an architecture motivated by Gomez-Bombarelli et al. The encoder consists of an Embedding layer that processes SELFIES tokens represented as integer arrays, three one-dimensional Convolutional layers (filters: 9,9,11; kernel size: 9,9,10), one Flatten layer, and one Dense layer that outputs parameters describing a 196-dimensional approximate posterior latent distribution.