In this paper we present a new method for high-level synthesis that enhances design flexibility, specialization and performance primarily conceived for programmable hardware. New programmable hardware devices often provide fast dedicated components that perform complex computations. Arbitrary complex computations can be efficiently extracted from the CDFG using our new graph matching constraint to produce final implementations that better suit the design to the targeted architecture. Our algorithm also reduces possible syntactic variances detecting semantically equivalent structures in the graph. This new graph matching constraint was integrated in our own Constraint Programming solver engine together with other constraints to naturally model the heterogeneous features present in the synthesis problem. The use of complex functional modules is taken into account in the optimization process during binding and scheduling yielding significantly shorter schedules and gains in terms of area and performance. We demonstrate our technique on a variety of HLS benchmarks and show that efficient design space exploration can be accomplished using this technique.