Novelty search is a state-of-the-art approach focusing on behavioural novelty, rewarding diverging, as opposed to pursuing static objectives. This is relevant for exploratory modelling and analysis, which focuses on exploration of model through open exploration or directed search. Novelty search as an open exploration strategy is being tested against proven methods such as latin hypercube sampling. Using existing evolutionary algorithms and a developed novelty function, the experiments focus on comparisson, impact of the number of functional evaluations and the impact of the goals of the evolutionary algorithm. Finally it can be concluded that novelty search finds novelties in the lake problem, which makes it a relevant search strategy, but not suited for indiviual exploration. That means that it would still be advised to use latin hypercube sampling for earlier exploration.