Metric indices support efficient similarity searches in metric spaces. This problem is central to many applications, including multimedia databases and repositories handling complex objects. Most metric indices are designed for main memory, and also most of them are static, that is, do not support insertions and deletions of objects. In this paper we introduce new metric indices for secondary memory that support updates, that is, they are dynamic. First, we show how the dynamic and memory-based Dynamic Spatial Approximation Tree (DSAT) can be extended to operate on secondary memory. Second, we design a dynamic and secondary-memory-based version of the static List of Clusters (LC), which performs well on high-dimensional spaces. The new structure is called Dynamic LC (DLC). Finally, we combine the DLC with the in-memory version of DSAT to create a third structure, Dynamic Set of Clusters (DSC), which improves upon the other two in various cases. We compare the new structures with the state of the art, showing that they are competitive and outstand in several scenarios, especially on spaces of medium and high dimensionality. (C) 2016 Elsevier Ltd. All rights reserved.