Identifying key timesteps in spatio-temporal datasets is essential for shaping the story that a simulation tells. The selected timesteps act as anchors for visualization, guiding parameter choices for rendering, animation, and analysis. While many sophisticated selection methods have been proposed, we show that the field has often leaned toward unnecessary complexity. In this work, we provide a survey of existing timestep selection strategies, illustrating their limited ability to balance quality and efficiency. Building on these insights, we introduce a deliberately simple approach based on greedy local search. Starting from uniformly spaced candidates, we iteratively shift selections to minimize reconstruction error under interpolation. Despite its simplicity, this method consistently yields high-quality subsets, enabling effective parameter tuning and exploratory visualization while achieving significantly lower computational cost than more elaborate techniques. Through quantitative comparisons across datasets and error metrics, we demonstrate that this purposeful simplicity can provide a better trade-off between quality and runtime than existing, more complex alternatives.