The ElMTree improves on this by using a metric indexing data structure , the List of Clusters (LC) , where randomly assigned objects of the search space are assigned as routing objects. The remaining objects are assigned to their closest routing object, and the distance from each routing object to its furthest child is stored. This ensures that objects which are in close proximity to one another in the metric space are found in similar clusters of the indexed LC. When similarity searches are performed, large regions of the LC may be completely disregarded. As the metric must obey the triangle inequality, we can use the covering radius to disregard any children of routing objects which must be further away than the desired cut-off value of proximity. This allows significantly fewer comparisons to be made in total, enabling search times which increase logarithmically in proportion to the total number of items in the search space. The ElMTree has been indexed with 1,065,165 unique compositions across 3,005,510 records from 5 published repositories . Despite this much larger search space, similarity queries typically find the k-closest matches in under a second. These response time improves this as a search tool, and enables comparisons against a wider range of reported compositions in high throughput computational workflows.