diff --git a/datafusion/physical-optimizer/src/coalesce_batches.rs b/datafusion/physical-optimizer/src/coalesce_batches.rs
index 5cf2c877c61a..e6d9835ec6bd 100644
--- a/datafusion/physical-optimizer/src/coalesce_batches.rs
+++ b/datafusion/physical-optimizer/src/coalesce_batches.rs
@@ -60,8 +60,7 @@ impl PhysicalOptimizerRule for CoalesceBatches {
             // wrap those ones with a CoalesceBatchesExec operator. An alternate approach here
             // would be to build the coalescing logic directly into the operators
             // See https://github.com/apache/datafusion/issues/139
-            let wrap_in_coalesce = plan_any.downcast_ref::<FilterExec>().is_some()
-                || plan_any.downcast_ref::<HashJoinExec>().is_some()
+            let wrap_in_coalesce = plan_any.downcast_ref::<HashJoinExec>().is_some()
                 // Don't need to add CoalesceBatchesExec after a round robin RepartitionExec
                 || plan_any
                     .downcast_ref::<RepartitionExec>()
diff --git a/datafusion/physical-plan/src/filter.rs b/datafusion/physical-plan/src/filter.rs
index 8e7c14f0baed..efa9643f7f04 100644
--- a/datafusion/physical-plan/src/filter.rs
+++ b/datafusion/physical-plan/src/filter.rs
@@ -24,6 +24,7 @@ use super::{
     ColumnStatistics, DisplayAs, ExecutionPlanProperties, PlanProperties,
     RecordBatchStream, SendableRecordBatchStream, Statistics,
 };
+use crate::coalesce::{BatchCoalescer, CoalescerState};
 use crate::common::can_project;
 use crate::{
     metrics::{BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet},
@@ -372,12 +373,15 @@ impl ExecutionPlan for FilterExec {
     ) -> Result<SendableRecordBatchStream> {
         trace!("Start FilterExec::execute for partition {} of context session_id {} and task_id {:?}", partition, context.session_id(), context.task_id());
         let baseline_metrics = BaselineMetrics::new(&self.metrics, partition);
+        let batch_size = context.session_config().batch_size();
         Ok(Box::pin(FilterExecStream {
             schema: self.schema(),
             predicate: Arc::clone(&self.predicate),
             input: self.input.execute(partition, context)?,
             baseline_metrics,
             projection: self.projection.clone(),
+            coalescer: BatchCoalescer::new(self.schema(), batch_size, None),
+            completed: false,
         }))
     }
 
@@ -451,6 +455,61 @@ struct FilterExecStream {
     baseline_metrics: BaselineMetrics,
     /// The projection indices of the columns in the input schema
     projection: Option<Vec<usize>>,
+    /// Coalescer to accumulate filtered batches up to target batch size
+    coalescer: BatchCoalescer,
+    completed: bool,
+}
+
+impl FilterExecStream {
+    fn poll_next_inner(
+        self: &mut Pin<&mut Self>,
+        cx: &mut Context<'_>,
+    ) -> Poll<Option<Result<RecordBatch>>> {
+        if self.completed {
+            return Poll::Ready(None);
+        }
+
+        let poll;
+        loop {
+            match ready!(self.input.poll_next_unpin(cx)) {
+                Some(Ok(batch)) => {
+                    let timer = self.baseline_metrics.elapsed_compute().timer();
+                    let filtered_batch = filter_and_project(
+                        &batch,
+                        &self.predicate,
+                        self.projection.as_ref(),
+                        &self.schema,
+                    )?;
+                    timer.done();
+                    // Skip entirely filtered batches
+                    if filtered_batch.num_rows() == 0 {
+                        continue;
+                    }
+
+                    let x = match self.coalescer.push_batch(filtered_batch) {
+                        CoalescerState::LimitReached => self.coalescer.finish_batch(),
+                        CoalescerState::TargetReached => self.coalescer.finish_batch(),
+                        CoalescerState::Continue => continue,
+                    };
+
+                    poll = Poll::Ready(Some(x));
+                    
+                    break;
+                }
+                None => {
+                    self.completed = true;
+                    poll = Poll::Ready(Some(self.coalescer.finish_batch()));
+                    break;
+                }
+                value => {
+                    poll = Poll::Ready(value);
+                    break;
+                }
+            }
+        }
+
+        poll
+    }
 }
 
 pub fn batch_filter(
@@ -500,31 +559,7 @@ impl Stream for FilterExecStream {
         mut self: Pin<&mut Self>,
         cx: &mut Context<'_>,
     ) -> Poll<Option<Self::Item>> {
-        let poll;
-        loop {
-            match ready!(self.input.poll_next_unpin(cx)) {
-                Some(Ok(batch)) => {
-                    let timer = self.baseline_metrics.elapsed_compute().timer();
-                    let filtered_batch = filter_and_project(
-                        &batch,
-                        &self.predicate,
-                        self.projection.as_ref(),
-                        &self.schema,
-                    )?;
-                    timer.done();
-                    // Skip entirely filtered batches
-                    if filtered_batch.num_rows() == 0 {
-                        continue;
-                    }
-                    poll = Poll::Ready(Some(Ok(filtered_batch)));
-                    break;
-                }
-                value => {
-                    poll = Poll::Ready(value);
-                    break;
-                }
-            }
-        }
+        let poll = self.poll_next_inner(cx);
         self.baseline_metrics.record_poll(poll)
     }