Fragment-based de novo methods constrain molecules to those that can be constructed from a fixed library of building block fragments, where fragments are often derived from published parent molecules using a suitable fragmentation scheme such as BRICS, a set of rules for decomposing and reassembling molecules using retrosynthetically-inspired disconnections . Fragment-based methods building on both population-based and gradient-based workflows have been described , but code is often unavailable. Notable examples for which code is available include Polishchuk (2020), which describes a framework for making chemically reasonable mutations (CReM) relying on interchangable fragments derived from matched molecular pairs to mutate, grow and link molecules; And more recently, LEADD , which uses a Lamarkian evolutionary algorithm to choose preferred fragment replacements from a fragment database. We find that fragment-based de novo design methods offer an attractive balance of practicality, interpretability and generative expressiveness, and evolutionary methods continue to produce impressive results despite the popularity of more complex methods Evolutionary algorithms (EAs) are highly effective optimizers but can be prone to stagnation by getting trapped in local optima . To remedy this, explicit measures to introduce population diversity are often included. MolFinder uses a minimum topological distance between molecules in the pool to ensure diversity is maintained; Graph-based elite patch illumination (GB-EPI) extends GB-GA to maintain a population GuacaMol was the first comprehensive benchmark for de novo molecular design and provides a convenient framework as well as a standardized training data set on which to assess the effectiveness of de novo molecular design approaches . In addition to generative distribution statistics, GuacaMol provides an applied set of 20 goal-directed tasks, which are powerful for assessing the usefulness of new generative chemistry models.