where ⟨•, •⟩ is the Euclidean inner product, ∥ • ∥ is the Euclidean norm, and σ 2 f is the kernel's signal variance hyperparameter. Several binary vector representations of molecules are available, but perhaps the most well known are extended-connectivity fingerprints (ECFPs). Atlas allows for use of any molecular representation based on binary vectors. Users must only specify, to the constructor of the planner, the indices that identify molecular parameters with the parameter space using the molecular params argument. Molecular parameters must be of type categorical, with options corresponding to unique molecules. Users may then define their descriptors as the corresponding binary vectors. We show a simple example in which we intend to minimize the aqueous solubility of molecules in the ESOL dataset. While it has been shown that using physicochemical descriptor-based representations of molecules in a BO setting can accelerate optimization rate, other studies have found that expert-crafted descriptors did not out-perform simpler representations like fingerprints or even one-hot-encodings. Given the apparent dependence of optimal molecular representation on the characteristics of the optimization problem at hand, Atlas provides users with the flexibility to represent molecular parameters in several ways.