Influence diagrams (ID) are a graphical computational model developed for decision making with uncertainty, based on probability inference. The unconstrained version of this model (UID) drops the restriction of linear order- ing of decisions. It adds expressiveness to the model, but it brings an exponential growth of complexity of the already computationally intensive algorithm for optimal ID evalua- tion. In this article, we present the first application for ex- act UID solving: Guido. We present the techniques we used to fight the computational complexity and how they affect the performance of the application.