Although originally devised in the field of image analysis, convolutional neural networks (CNNs) are increasingly finding applications outside the image domain. In particular, a number of studies over the last year have made a convincing case for the use of CNNs within the field of molecular modelling. As an example of these recent developments, we will present our work on using convolutions to predict mutation-induced changes-of-stability (ddgs) in proteins. We will demonstrate how a simple convolutional model using a purely data-driven approach achieves performance comparable to that of state-of-the-art methods in the field. Finally, we will discuss current theoretical developments in the area of convolutions, including the quest for rotational equivariance.