Gryffin has two modes: a naive, a static, and a dynamic approach. Naive Gryffin ignores descriptors; static Gryffin includes the descriptors "as given"; dynamic Gryffin also includes descriptors and furthermore infers a transformation of the initial descriptors by a one-layer neural network during the optimisation process. To simultaneously optimise categorical and continuous input variables, Gryffin internally integrates the Phoenics framework. In our benchmarks, we use Gryffin with the following settings which are similar to the default configuration of Gryffin: "auto_desc_gen" = True (dynamic Gryffin), "batches" = 1, "parallel" = true, "boosted" = true, "sampler" = "uniform", "softness" = 0.001, "continuous_optimiser" = "adam", "cate-gorical_optimiser" = "naive", " sampling_strategies" = "4". We use this number of sampling strategies as we found it to work well with Gryffin. Additionally, we found that using precreated descriptors often led to dimensional explosion and caused the strategy to occupy large amounts of RAM.