AI Systems are becoming ubiquitous and assuming different roles in our lives: they can act as recommendation systems in multiple contexts, they can work as personal assistants, they can tag images, etc. Whilst their contributions are clear, the reasoning behind them are not so transparent and may need explanations. This need for interpretability created new challenges for developers and designers from different communities. Visualizing multidimensional data and exploring the objects’ similarities can help with the explainability of an AI system. In this work, we discuss the visual inspection of high-dimensional objects being complementary to machine learning techniques. We present RAVA (Reservoir Analogues Visual Analytics), a system that employs machine learning and visual analytics techniques to empower geoscientists in the task of finding similar reservoirs.