In recent years, there has been a trend towards reconfigurable fabrics at the intersection between field-programmable gate arrays (FPGAs) and coarse-grained reconfigurable arrays (CGRAs): using FPGA-like interconnect but word-based and built around coarse-grained primitives. These architectures often employ complex clusters with far more heterogeneous resources than FPGAs or typical CGRAssometimes over a hundred primitives, most of which are bypassable. As a result, clustering, the problem of covering the application netlist with architecture clusters, is a key challenge for design tools targeting these fabrics. Clustering is analogous to the instruction selection problem in CISC architectures, albeit with orders of magnitude more complex "instructions". In this work, we propose a two-phase, architectureagnostic clustering algorithm that scales to highly complex architecture clusters. The first phase enumerates potential cluster matches in the application netlist using a strategy based on an abstract decision tree. The second phase selects a cover from the enumerated matches. We show that our algorithm effectively prunes the search space for complex clusters, scales well to circuits composed of many clusters, and achieves better clustering quality than CLUMAP, a state-of-the-art CGRA clustering algorithm, for the simple cases that CLUMAP can handle.