Finding transition states and diffusion pathways is essential to understand the evolution of materials and chemical reactions. Such characterization is hampered by the heavy computation costs associated with exploring energy landscapes at ab-initio accuracy. Here, we revisit the activation-relaxation technique (ARTn) to considerably reduce its costs when used with density functional theory (DFT) and propose three adapted versions of the algorithm to efficiently (i) explore the energy landscape of complex materials with the nowledge of a single minimum (ARTn); (ii) identify a transition state when two minima or a guess transition state are given (refining ART or r-ART) and (iii) reconstruct complex pathways between two given states (directed ART or d-ART). We show the application of these three variants on benchmark examples and on various complex defects in silicon. For the later, the presented improvements to ART leads to much more precise transition states while being two to six times faster than the commonly used string methods such as the Climbing Image Nudged Elastic Band method (CI-NEB).