In this article we have introduced the binary-encoded SMARTS (BESMARTS) approach to build hierarchical SMARTS models and paved a way towards automated chemical perception for force field design. Here we show the utility and performance of the method by creating a SMARTS hierarchy based on either categorical or continuous data. For categorical data, the chemical environments have been previously labeled by some other model and we aim to find SMARTS patterns that map to the labels. The two models that we examined are the atom types from GAFF and the labels from Sage. We then apply the BESMARTS approach to build a SMARTS hierarchy that clusters bond lengths from a set of molecules optimized using DFT. This result is an example of how BESMARTS could be used as a tool to help determine bond parameterization as part of building a force field from scratch. Below we discuss the three cases.