The　Clustered　Team　Orienteering　Problem　(CluTOP)　extends　the　classic　Clustered　Orienteering　Problem　by　considering　the　use　of　multiple　vehicles.　The　problem　is　known　to　be　NP-hard　and　can　be　used　to　formulate　many　real-life　applications.　This　work　presents　a　highly　effective　multi-level　memetic　search　for　CluTOP　that　combines　a　backbone-based　edge　assembly　crossover　to　generate　promising　offspring　solutions　with　an　effective　bilevel　synergistic　local　search　procedure　at　both　cluster　and　customer　levels　to　improve　offspring　solutions.　Other　novel　features　of　the　proposed　approach　include　a　joint　use　of　three　specific　hash　functions　to　identify　the　tabu　status　of　candidate　solutions　at　the　cluster　level,　a　multi-neighborhood　search　with　inter-route　and　intra-route　optimization　at　the　customer　level,　a　pre-processing　neighborhood　reduction　strategy　to　avoid　examining　non-promising　candidate　solutions,　and　a　strategy　for　controlled　exploration　of　infeasible　solutions.　Extensive　experimental　results　on　1848　benchmark　instances　convincingly　demonstrate　high　competitiveness　of　the　approach　in　terms　of　both　solution　quality　and　computational　time,　compared　to　the　state-of-the-art　heuristics　from　the　literature.　In　particular,　the　proposed　algorithm　improves　upon　the　existing　best-known　solutions　for　294　instances,　while　matching　the　previous　best-known　results　for　all　but　3　of　the　remaining　instances.　To　gain　further　insights　into　the　algorithm＇s　performance,　additional　experiments　are　conducted　to　analyze　its　main　components.