Filter data structures are widely used to answer approximate set-membership queries in various areas of computer science. A filter needs to be allocated with a given capacity in advance to provide a guarantee over the false positive rate and performance. However, in many applications, the data size is not known in advance, requiring filters to expand dynamically. We show that existing methods for expanding filters do not scale in terms of performance or the false positive rate. This talk will cover InfiniFilter (SIGMOD 2023) and Aleph Filter (VLDB 2024), two new expandable filters that address these challenges.