Microbial communities underpinning the operation of wastewater treatment plants are particularly challenging targets for MAG analysis due to their high eco-biological complexity, and remain important, albeit understudied and play a key role in mediating the interactions between human and natural ecosystems. We consider strategies for recovery of MAG sequence from time series metagenome surveys of full-scale activated sludge microbial communities and generate MAG catalogues using: multiple individual sample assemblies, two variations on multi-sample co-assembly, and a recently published MAG recovery workflow using deep learning (VAMB). We obtain a total of just under 9,100 draft genomes, which collapse to around 3,100 non-redundant genomic clusters. Here, we provide additional data not included in NCBI submission (BioProject Accession PRJNA731554): 1. all_25_assemblies.tar.gz: contains 24 individual short-reads assemblies sequences (FASTA) from each of the 24 samples and one co-assembly sequence from the combined 24 samples. 2. coassembly_multi_bam.tar.gz: contains all 1,712 MAGs sequences (FASTA) obtained from co-assembly and Metabat2 binning workflow using coverage profiles generated across all 24 samples. 3. coassembly_single_bam.tar.gz: contains 1,997 MAGs sequences (FASTA) obtained from co-assembly and Metabat2 binning workflow using the entire read set treated as a single meta-sample. 4. individual_assemblies.tar.gz: contains 3,429 MAGs sequences (FASTA) obtained from individual assemblies and Metabat2 binning workflow. 5. vamb.tar.gz: contains 1,941 MAGs sequences (FASTA) obtained from individual assemblies and VAMB binning workflow.