Self-organisation occurs in many physical, chemical and biological systems, as well as in artificial systems like the Game of Life. Yet, these systems are still full of mysteries and we are far from fully grasping what structures can self-organize, how to represent and classify them, and how to predict their evolution. In this blog post, we present our recent paper which formulates the problem of automated discovery of diverse self-organized patterns in such systems. Using a continuous Game of Life as a testbed, we show how intrinsically-motivated goal exploration processes, initially developed for learning of inverse models in robotics, can efficiently be transposed to this novel application area.