Abstract: Most past work in multiple instance learning (MIL), which maps groups of instances to classification labels, has focused on settings in which the order of instances does not contain information. In this paper, we define MIL with \textit{absolute} position information: tasks in which instances of importance remain in similar positions across bags. Such problems arise, for example, in MIL with medical images in which there exists a common global alignment across images (e.g., in chest x-rays the heart is in a similar location). We also evaluate the performance of existing MIL methods on a set of new benchmark tasks and two real data tasks with varying amounts of absolute position information. We find that, despite being less computationally efficient than other approaches, transformer-based MIL methods are more accurate at classifying tasks with absolute position information. Thus, we investigate the ability of positional encodings, a mechanism typically only used in transformers, to improve the accuracy of other MIL approaches. Applied to the task of identifying pathological findings in chest x-rays, when augmented with positional encodings, standard MIL approaches perform significantly better than without (AUROC of 0.799, 95\% CI: [0.791, 0.806] vs. 0.782, 95\% CI: [0.774, 0.789]) and on-par with transformer-based methods (AUROC of 0.797, 95\% CI: [0.790, 0.804]) while being 10 times faster. Our results suggest that one can efficiently and accurately classify MIL data with standard approaches by simply including positional encodings.