Most existing fuzzing tools use edge coverage to identify interesting inputs and guide the expansion of the corpus. This coverage signal is convenient because it is bounded in size. Once fuzzing discovers all reachable edges, however, this form of coverage stops being useful. To keep providing a useful guidance to the fuzzer we can add additional signals, such as call stacks, bounded execution paths, arguments to comparison instructions, and signals derived from anomaly detection. Most of these signals can generate a large amount of data that the fuzzer needs to deal with which can have a drastic impact on the computational resources required. It is still tempting to use these rich signals. In the SiliFuzz project we have used rich coverage signals to uncover bugs that were hidden otherwise. In this talk we will discuss approaches to scaling fuzzing with rich coverage signals in a new fuzzing engine called Centipede.