This paper presents a new method for exploring conformational energy landscapes. The method, called T-RRT, combines ideas from robotics path planning and statistical physics. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: On the one hand, it is naturally biased toward yet unexplored regions of the space. On the other hand, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved thanks to a self-tuning mechanism. The method is able to efficiently find both energy minima and transition paths between them. As a proof of concept, the method is applied to several academic benchmarks and to the alanine dipeptide.