We secondly generate SELFIES by drawing from the training distribution of sequences and shuffling tokens internally, randomly placing tokens into new sequence positions (Figure B). Since we sample initial SELFIES from the training set, this generator contains more training distribution information than naive random because the shuffling process preserves composition while altering the positional arrangement of tokens.