In order to compute the thermodynamic weights of the diﬀerent metastable conformations of a molecule, we want to approximate the molecule’s Boltzmann distribution π in a reasonable time. This is an essential issue in computational drug design. The energy landscape of active biomolecules is generally very rough with a lot of high barriers and low regions. Many of the algorithms that perform such samplings (e.g. the hybrid Monte Carlo method) have diﬃculties with such landscapes. They are trapped in low-energy regions for a very long time and cannot overcome high barriers. Moving from one low-energy region to another is a very rare event. For these reasons, the distribution of the generated sampling points converges very slowly against the thermodynamically correct distribution of the molecule. The idea of ConfJump is to use a priori knowledge of the localization of low-energy regions to enhance the sampling with artiﬁcial jumps between these low-energy regions. The artiﬁcial jumps are combined with the hybrid Monte Carlo method. This allows the computation of some dynamical properties of the molecule. In ConfJump, the detailed balance condition is satisﬁed and the mathematically correct molecular distribution is sampled.