diff --git a/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp b/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
index aa6da0a4f..4ce0dcebb 100644
--- a/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
+++ b/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
@@ -299,9 +299,6 @@ llvm::SmallVector<llvm::StringRef> parallelIteratorType(int n) {
 /// specification for the iteration dimensions and appropriate operations
 /// managing the accumulator of `linalg.generic`.
 ///
-/// The current implementation does not rely on 'tensor.extract_slice'
-/// because of a bug in lowering this operation.
-///
 /// Example:
 /// %res = "FHELinalg.apply_mapped_lookup_table"(%t, %luts, %map)
 /// : (tensor<2x3x!FHE.eint<2>>, tensor<5x4xi64>, tensor<2x3xindex>)
@@ -317,17 +314,8 @@ llvm::SmallVector<llvm::StringRef> parallelIteratorType(int n) {
 /// tensor<2x3x!TFHE.glwe<{_,_,_}{2}>>) {
 ///          ^bb0(%arg3: !TFHE.glwe<{_,_,_}{2}>, %lut_idx: index, %arg5:
 ///          !TFHE.glwe<{_,_,_}{2}>):  // no predecessors
-///          // SHOULD BE
 ///          %lut = tensor.extract_slice %arg1[%[[LUTIDX]], 0] [1,4] [1, 1]
 ///                 : tensor<5x4xi64> to tensor<4xi64>
-///          // BUT IS
-///          %i0 = arith.constant 0 : index
-///          ...
-///          %i3 = arith.constant 3 : index
-///          %e0 = tensor.extract %arg5[%lut_idx, %i0] : tensor<5x4xi64>
-///          ...
-///          %e3 = tensor.extract %arg5[%lut_idx, %i3] : tensor<5x4xi64>
-///          %lut = tensor.from_elements %e0, ..., %e3 : tensor<4xi64>
 ///          %res  = "TFHE.apply_lookup_table"(%arg3, %[[LUT]])
 ///                    {baseLogBS = -1 : i32, baseLogKS = -1 : i32,
 ///                    glweDimension = -1 : i32,
@@ -383,49 +371,23 @@ struct FHELinalgApplyMappedLookupTableToLinalgGeneric
 
     // Create the body of the `linalg.generic` op
     // %arg0 is an element of t (encrypted int)
-    // %arg1 is an element of map (i64)
+    // %arg1 is the lut index (i64)
     // %arg2 is the output element
     auto lambdaBlock = [&](mlir::OpBuilder &nestedBuilder,
                            mlir::Location nestedLoc,
                            mlir::ValueRange blockArgs) {
       auto tElmt = blockArgs[0];
       auto lutIdx = blockArgs[1];
-      auto indexTy = rewriter.getIndexType();
 
       // %lut = extract_slice %luts[%lutIdx, 0][1, lutSize][1, 1]  :
       // tensor<NxKxi64> to tensor<Kxi64>
-      mlir::Value lut;
-      const bool WORKAROUND_EXTRACT_SLICE = true;
-      if (!WORKAROUND_EXTRACT_SLICE) {
-        sliceArg offsets{lutIdx, _0_};
-        sliceArg sizes{_1_, lutSizeValue};
-        sliceArg strides{_1_, _1_};
-        auto lutTy = mlir::RankedTensorType::get(
-            {static_cast<int64_t>(lutSize)}, lutElmtTy);
-        lut = nestedBuilder.create<tensor::ExtractSliceOp>(
-            loc, lutTy, luts, offsets, sizes, strides);
-      } else {
-        // WORKAROUND BEGIN
-        // A bug in linalg-bufferize prevents rank reduction in extract_slice
-        // Reshaping does not work either or is too complicated so let's rebuild
-        // the tensor from scratch
-        llvm::SmallVector<mlir::Value> consts;
-        llvm::SmallVector<mlir::Value> extracts;
-        for (int i = 0; i < lutSize; i++) {
-          consts.push_back(
-              // %5 = arith.constant(<i> : index) : index
-              nestedBuilder.create<mlir::arith::ConstantOp>(
-                  loc, indexTy, rewriter.getIndexAttr(i)));
-        }
-        for (int i = 0; i < lutSize; i++) {
-          extracts.push_back(
-              // %8 = tensor.extract %luts[<lutIdx>, <i>] : ...
-              nestedBuilder.create<tensor::ExtractOp>(
-                  loc, luts, mlir::ValueRange({lutIdx, consts[i]})));
-        }
-        // %12 = tensor.from_elements %8, ... : ...
-        lut = nestedBuilder.create<tensor::FromElementsOp>(loc, extracts);
-      } // WORKAROUND END
+      sliceArg offsets{lutIdx, _0_};
+      sliceArg sizes{_1_, lutSizeValue};
+      sliceArg strides{_1_, _1_};
+      auto lutTy = mlir::RankedTensorType::get({static_cast<int64_t>(lutSize)},
+                                               lutElmtTy);
+      mlir::Value lut = nestedBuilder.create<tensor::ExtractSliceOp>(
+          loc, lutTy, luts, offsets, sizes, strides);
       // %res1 = apply_lookup_table %arg0 %lut
       auto lookup = nestedBuilder.create<FHE::ApplyLookupTableEintOp>(
           loc, elementTy, tElmt, lut);