We review the class of partial‐propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial‐propensity SSAs are composed of and how partial‐propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial‐propensity variant of Gillespie’s original direct method and that of the SSA with composition‐rejection sampling (SSA‐CR). Partial‐propensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial‐propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations.