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Modern computer games have become very complex, so they can benefit from automated testing. However, their huge and fine grained interaction space makes them very challenging for automated testing algorithms. Having a model of a system would greatly improve the effectiveness of a testing algorithm. However, manually constructing a model is expensive and time-consuming. This paper proposes an online agent-based search approach to solve common testing tasks for computer games, in particular games that involve elements of world navigation and exploration. On the fly, the approach also constructs a model of the system, which is then exploited to solve the given testing task. The effectiveness of the approach is studied via a case study called Lab Recruits and its simulation of another game called Dungeons and Dragons Online. The study showed that the approach is superior in its ability to complete testing tasks and its completion time compared to evolutionary algorithm, Q-learning and MCTS. This paper extends a previous work presented in ATEST by including evaluation on large game levels, evaluation of the achieved coverage and fault detection and the aforementioned comparison with other algorithms.