In many Bayesian inverse problems the change from prior to posterior is confined to a low-dimensional subspace of the parameter space. We explore gradient-based dimension reduction techniques to identify this crucial subspace, with the primary aim of enhancing the efficiency of MCMC methods employed in tackling the inverse problem.