The recently proposed paradigm of learned Bloom filters (LBF) seems to offer significant advantages over 
traditional Bloom filters in terms of low memory footprint and overall performance as evidenced by empirical 
evaluations over static data. Its behavior in presence of updates to the set of keys being stored in Bloom 
filters is not very well understood. At the same time, maintaining the false positive rates (FPR) of traditional 
Bloom filters in presence of dynamics has been studied and extensions to carefully expand memory footprint of 
the filters without sacrificing FPR have been proposed. Building on these, we propose two distinct approaches 
for handling data updates encountered in practical uses of LBF: (i) CA-LBF, where we adjust the learned model 
(e.g., by retraining) to accommodate the new ``unseen'' data, resulting in classifier adaptive methods, and 
(ii) IA-LBF, where we replace the traditional Bloom filter with its adaptive version while keeping the learned 
model unchanged, leading to an index adaptive method. In this paper, we explore these two approaches in detail 
under incremental workloads, evaluating them in terms of their adaptability, memory footprint and false positive 
rates.  Our empirical results using a variety of datasets and learned models of varying complexity show that our 
proposed methods' ability to handle incremental updates is quite robust.