Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discoveries. The last few decades have seen the consolidation of data-driven scientific discoveries. However, in order to leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key current challenge for machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating uncertainty and generating sets of diverse candidates. This motivated a new machine learning probabilistic framework called GFlowNets, which can be applied both for modelling and for the experimental design components of the active learning theory-experiment-analysis loop. GFlowNets learn to sample proportionally to a reward function, which enables sampling diverse, high-reward candidates. Equipped with the capabilities of deep learning, GFlowNets can also be used to perform efficient and amortized probabilistic inference, consistent with the knowledge captured in the world model, trained from acquired experimental data. This talk briefly introduced GFlowNets and its relevance for scientific discovery, in particular when used together with active learning.