NAND Flash has become the most popular persistent data storage medium for mobile and embedded devices and is even being considered as a credible competitor for traditional disks. The hardware characteristics of NAND Flash (e.g. page granularity for read/write with a block-erase-before-rewrite constraint, limited number of erase cycles) preclude in-place updates. Previous works adapted traditional indexing methods to cope with these constraints mainly by deferring index updates thanks to a log and batching them to decrease the number of rewrite operations in Flash. These methods introduce a complex trade-off between read and write performance and neglect negative side-effects on the RAM consumption, Flash consumption and garbage collection cost. In this paper, we propose a new alternative for indexing Flash-resident data, designed from the outset to exploit the peculiarities of NAND Flash. This ap-proach, called PBFilter, organizes the index structure in a pure sequential way to avoid the side-effects mentioned above. Key lookups are sped up thanks to two principles called Summariza-tion and Partitioning. We instantiate these principles by data structures and algorithms based on Bloom Filters and show the effectiveness of this approach through a comprehensive perform-ance analysis. PBFilter can be instantiated with different Summarization and Partitioning algo-rithms opening up a new way to think about indexing Flash-resident data.