In radiation protection studies, the goal is to estimate the response of a detector exposed to a strongly attenuated radiation field. Monte Carlo (MC) particle transport codes give the possibility to efficiently solve for such responses using several variance-reduction (VR) methods that help allocating more CPU time to the simulation of highly contributing histories. The TRIPOLI-4® MC particle transport code offers two main methods, the exponential transform and adaptive multilevel splitting (AMS), which rely on the definition of a suitable importance map. In this paper, we present an implementation of a generalized Consistent Adjoint Driven Importance Sampling (CADIS) methodology for TRIPOLI-4. The implementation relies on coupling with the IDT code, a deterministic solver for the Boltzmann adjoint transport equation, for the generation of importance maps. We study the performance of both VR methods present in TRIPOLI-4 in this setting. In particular, to our knowledge, this is the first time that a CADIS-like methodology has been applied to AMS.