In this paper, we address this fundamental problem with a novel approach to sampling over atom or fragment types typically used in biomolecular force fields, so that these definitions can ultimately be learned automatically without a human expert. In particular, we introduce methods of sampling over hierarchical chemical perception trees. That is, we organize force field atom or fragment types into hierarchical trees, such that child types contain more specific types than parent types. We utilize the SMARTS and SMIRKS substructure definition languages to specify atom types and more general fragment types which may be associated with distinct numerical parameters. Then, we show how a Monte Carlo scheme can be used to sample over chemical perception trees defined using these languages. Both tools for sampling chemical perception trees, SMARTY (atom types) and SMIRKY (fragment types), are freely available to the community on GitHub along with Open Force Field Initiative software (). As a proof of principle for this approach, we compare the chemical complexity of sampled atom and fragment types to those in existing force fields. These algorithms set the stage for future applications in which the scoring function will be based on the agreement of simulations with experimental, quantum, or other reference data.