Our brains are able to exploit coarse physical models of fluids to quickly adapt and solve everyday manipulation tasks. However, developing such capability in robots, so that they can autonomously manipulate fluids adapting to different conditions remains a challenge. In this talk, I will present how a Robot can use an internal model based on simulation to infer properties of real liquids, via direct and indirect interactions, using Bayesian Optimization. I will introduce the challenges of using approximate simulation as a forward model, and the usefulness of the estimations to perform a pouring task on different containers and liquid properties.