We describe FragGT, an EA capable of generating molecules in a wide range of practical settings relevant to drug discovery. We view molecules as a set of fragment genes, and introduce a concept of gene-types to classify fragments according to the types of connections they can make with adjacent fragments. We report performance on the GuacaMol goal-directed benchmark suite and release code under an open license as part of the guacamol baselines GitHub repository (github.com/BenevolentAI/guacamol baselines).