When attempting to understand where people look during scene perception, researchers typically focus on the relative contributions of low- and high-level cues. Computational models of the contribution of low-level features to fixation selection, with modifications to incorporate top-down sources of information have been abundant in recent research. However, we are still some way from a model that can explain many of the complexities of eye movement behaviour. Here we show that understanding biases in how we move the eyes can provide powerful new insights into the decision about where to look in complex scenes. A model based solely on these biases and therefore blind to current visual information outperformed popular salience-based approaches. Our data show that incorporating an understanding of oculomotor behavioural biases into models of eye guidance is likely to significantly improve our understanding of where we choose to fixate in natural scenes.