The increasing complexity and size of digital designs in conjunction with the lack of a potent verification methodology that can effectively cope with this trend, continues to inspire engineers and academics in seeking ways to further automate design verification. In an effort to increase performance and to decrease engineering effort, research has turned to Artificial Intelligence techniques for effective solutions. The generation of tests for simulation-based verification can be guided by Machine Learning techniques. In fact, recent advances demonstrate that embedding Machine Learning techniques into a Coverage Directed Test Generation framework can effectively automate the test generation process, making it more effective and less error prone. This is a review of some of the most prominent approaches in this field, aiming to take stock and to further stimulate more directed research in this area.