Developers today use significant amounts of open source code, surfacing the need for ways to automatically audit and upgrade library dependencies and leading to the emergence of Software Composition Analysis (SCA). SCA products are concerned with three tasks: discovering dependencies, checking the reachability of vulnerable code for false positive elimination, and automated remediation. The latter two tasks rely on call graphs of library and application code to check whether vulnerable methods found in the open source components are called by applications. However, statically-constructed call graphs introduce both false positives and false negatives on real-world projects. In this paper, we develop a novel, modular means of combining statically- and dynamically-constructed call graphs via instrumentation to improve the performance of false positive elimination. Our experiments indicate significant performance improvements, but that instrumentation-based call graphs are less readily applicable in practice.